
import refinitiv.data as rd  # This is LSEG's Data and Analytics' API wrapper, called the Refinitiv Data Library for Python.
from refinitiv.data.content import historical_pricing  # We will use this Python Class in `rd` to show the Implied Volatility data already available before our work.
from refinitiv.data.content import search  # We will use this Python Class in `rd` to fid the instrument we are after, closest to At The Money.
import refinitiv.data.content.ipa.financial_contracts as rdf  # We're going to need thtis to use the content layer of the RD library and the calculators of greeks and Impl Volat in Instrument Pricing Analytics (IPA) and Exchange Traded Instruments (ETI)
from refinitiv.data.content.ipa.financial_contracts import option  # We're going to need thtis to use the content layer of the RD library and the calculators of greeks and Impl Volat in IPA & ETI

import numpy as np  # We need `numpy` for mathematical and array manipilations.
import pandas as pd  # We need `pandas` for datafame and array manipilations.
import calendar  # We use `calendar` to identify holidays and maturity dates of intruments of interest.
import pytz  # We use `pytz` to manipulate time values aiding `calendar` library. to import its types, you might need to run `!python3 -m pip install types-pytz`
import pandas_market_calendars as mcal  # Used to identify holidays. See `https://github.com/rsheftel/pandas_market_calendars/blob/master/examples/usage.ipynb` for info on this market calendar library
from datetime import datetime, timedelta, timezone  # We use these to manipulate time values
from dateutil.relativedelta import relativedelta  # We use `relativedelta` to manipulate time values aiding `calendar` library.
import requests  # We'll need this to send requests to servers vie a the delivery layer - more on that below

# `plotly` is a library used to render interactive graphs:
import plotly.graph_objects as go
import plotly.express as px  # This is just to see the implied vol graph when that field is available
import matplotlib.pyplot as plt  # We use `matplotlib` to just in case users do not have an environment suited to `plotly`.
from IPython.display import clear_output, display  # We use `clear_output` for users who wish to loop graph production on a regular basis. We'll use this to `display` data (e.g.: pandas data-frames).
from plotly import subplots
import plotly

# Let's authenticate ourseves to LSEG's Data and Analytics service, Refinitiv:
try:  # The following libraries are not available in Codebook, thus this try loop
    rd.open_session(config_name="C:\\Example.DataLibrary.Python-main\\Example.DataLibrary.Python-main\\Configuration\\refinitiv-data.config.json")
    rd.open_session("desktop.workspace")
except:
    rd.open_session()


mcal.__version__

'4.1.0'


print(f"Here we are using the refinitiv Data Library version {rd.__version__}")

Here we are using the refinitiv Data Library version 1.0.0b24


!python -V

Python 3.8.2


response1 = search.Definition(
    view = search.Views.SEARCH_ALL, # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`
    query=".STOXX50E",
    select="DocumentTitle, RIC, StrikePrice, ExchangeCode, ExpiryDate, UnderlyingAsset, " +
            "UnderlyingAssetName, UnderlyingAssetRIC, ESMAUnderlyingIndexCode, RCSUnderlyingMarket" +
            "UnderlyingQuoteName, UnderlyingQuoteRIC, InsertDateTime, RetireDate",
    filter="RCSAssetCategoryLeaf eq 'Option' and RIC eq 'STX*' and DocumentTitle ne '*Weekly*'  " +
    "and CallPutOption eq 'Call' and ExchangeCode eq 'EUX' and " +
    "ExpiryDate ge 2022-07-10 and ExpiryDate lt 2023-07-22",  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.
    top=100).get_data()
searchDf1 = response1.data.df


searchDf1


currentUnderlyingPrc = rd.get_history(
    universe=[searchDf1.UnderlyingQuoteRIC[0][0]],
    fields=["TRDPRC_1"],
    interval="tick").iloc[-1][0]


currentUnderlyingPrc

4215.43


searchDf1.iloc[(searchDf1['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]]


def Get_exp_dates(year, days=True, mcal_get_calendar='EUREX'):
    '''
    Get_exp_dates Version 2.0:

    This function gets expiration dates for a year for NDX options, which are the 3rd Fridays of each month.

    Changes
    ----------------------------------------------
    Changed from Version 1.0 to 2.0: Jonathan Legrand changed Haykaz Aramyan's original code to allow
        (i) function name changed from `get_exp_dates` to `Get_exp_dates`
        (ii) for the function's holiday argument to be changed, allowing for any calendar supported by `mcal.get_calendar` and defaulted to 'EUREX' as opposed to 'CBOE_Index_Options' and
        (iii) for the function to output full date objects as opposed to just days of the month if agument days=True.

    Dependencies
    ----------------------------------------------
    Python library 'pandas_market_calendars' version '3.2'.
    pandas_market_calendars as mcal version '4.1.0'.

    Parameters
    -----------------------------------------------
    Input:
        year(int): year for which expiration days are requested

        mcal_get_calendar(str): String of the calendar for which holidays have to be taken into account. More on this calendar (link to Github checked 2022-10-11): https://github.com/rsheftel/pandas_market_calendars/blob/177e7922c7df5ad249b0d066b5c9e730a3ee8596/pandas_market_calendars/exchange_calendar_cboe.py
            Default: mcal_get_calendar='EUREX'

        days(bool): If True, only days of the month is outputed, else it's dataeime objects
            Default: days=True

    Output:
        dates(dict): dictionary of expiration days for each month of a specified year in datetime.date format.
    '''

    # get CBOE market holidays
    EUREXCal = mcal.get_calendar(mcal_get_calendar)
    holidays = EUREXCal.holidays().holidays

    # set calendar starting from Saturday
    c = calendar.Calendar(firstweekday=calendar.SATURDAY)

    # get the 3rd Friday of each month
    exp_dates = {}
    for i in range(1, 13):
        monthcal = c.monthdatescalendar(year, i)
        date = monthcal[2][-1]
        # check if found date is an holiday and get the previous date if it is
        if date in holidays:
            date = date + timedelta(-1)
        # append the date to the dictionary
        if year in exp_dates:
            ### Changed from original code from here on by Jonathan Legrand on 2022-10-11
            if days: exp_dates[year].append(date.day)
            else: exp_dates[year].append(date)
        else:
            if days: exp_dates[year] = [date.day]
            else: exp_dates[year] = [date]
    return exp_dates


fullDates = Get_exp_dates(2022, days=False)
dates = Get_exp_dates(2022)
fullDatesStrDict = {i: [fullDates[i][j].strftime('%Y-%m-%d')
                        for j in range(len(fullDates[i]))]
                    for i in list(fullDates.keys())}
fullDatesDayDict = {i: [fullDates[i][j].day
                        for j in range(len(fullDates[i]))]
                    for i in list(fullDates.keys())}


print(fullDates)

{2022: [datetime.date(2022, 1, 21), datetime.date(2022, 2, 18), datetime.date(2022, 3, 18), datetime.date(2022, 4, 14), datetime.date(2022, 5, 20), datetime.date(2022, 6, 17), datetime.date(2022, 7, 15), datetime.date(2022, 8, 19), datetime.date(2022, 9, 16), datetime.date(2022, 10, 21), datetime.date(2022, 11, 18), datetime.date(2022, 12, 16)]}


print(fullDatesStrDict)

{2022: ['2022-01-21', '2022-02-18', '2022-03-18', '2022-04-14', '2022-05-20', '2022-06-17', '2022-07-15', '2022-08-19', '2022-09-16', '2022-10-21', '2022-11-18', '2022-12-16']}


print(dates)

{2022: [21, 18, 18, 14, 20, 17, 15, 19, 16, 21, 18, 16]}


print(fullDatesDayDict)

{2022: [21, 18, 18, 14, 20, 17, 15, 19, 16, 21, 18, 16]}


x = 15


timeOfCalcDatetime = datetime.now()  # For now, we will focuss on the use-case where we are calculating values for today; later we will allow for it historically for any day going back a few business days.
timeOfCalcStr = datetime.now().strftime('%Y-%m-%d')
timeOfCalcStr

'2023-03-29'


fullDatesAtTimeOfCalc = Get_exp_dates(timeOfCalcDatetime.year, days=False)  # `timeOfCalcDatetime.year` here is 2023
fullDatesAtTimeOfCalcDatetime = [
    datetime(i.year, i.month, i.day)
    for i in fullDatesAtTimeOfCalc[list(fullDatesAtTimeOfCalc.keys())[0]]]


print(fullDatesAtTimeOfCalcDatetime)

[datetime.datetime(2023, 1, 20, 0, 0), datetime.datetime(2023, 2, 17, 0, 0), datetime.datetime(2023, 3, 17, 0, 0), datetime.datetime(2023, 4, 21, 0, 0), datetime.datetime(2023, 5, 19, 0, 0), datetime.datetime(2023, 6, 16, 0, 0), datetime.datetime(2023, 7, 21, 0, 0), datetime.datetime(2023, 8, 18, 0, 0), datetime.datetime(2023, 9, 15, 0, 0), datetime.datetime(2023, 10, 20, 0, 0), datetime.datetime(2023, 11, 17, 0, 0), datetime.datetime(2023, 12, 15, 0, 0)]


expiryDateOfInt = [i for i in fullDatesAtTimeOfCalcDatetime
                   if i > timeOfCalcDatetime + relativedelta(days=x)][0]
expiryDateOfInt

datetime.datetime(2023, 4, 21, 0, 0)


response2 = search.Definition(
    view=search.Views.SEARCH_ALL, # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`
    query=".STOXX50E",
    select="DocumentTitle, RIC, StrikePrice, ExchangeCode, ExpiryDate, UnderlyingAsset, " +
            "UnderlyingAssetName, UnderlyingAssetRIC, ESMAUnderlyingIndexCode, RCSUnderlyingMarket" +
            "UnderlyingQuoteName, UnderlyingQuoteRIC, InsertDateTime, RetireDate",
    filter="RCSAssetCategoryLeaf eq 'Option' and RIC eq 'STX*' and DocumentTitle ne '*Weekly*' " +
    "and CallPutOption eq 'Call' and ExchangeCode eq 'EUX' and " +
    f"ExpiryDate ge {(expiryDateOfInt - relativedelta(days=1)).strftime('%Y-%m-%d')} " +
    f"and ExpiryDate lt {(expiryDateOfInt + relativedelta(days=1)).strftime('%Y-%m-%d')}",  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.
    top=10000,
).get_data()
searchDf2 = response2.data.df


searchDf2


searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]]


instrument = searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]].RIC.values[0]
instrument

'STXE42250D3.EX'


## Example Options:

# instrument_1 = 'SPXv212240000.U'
# instrument_2 = 'STXE35500J2.EX'  # Eurex Dow Jones EURO STOXX 50 Index Option 3550 Call Oct 2022, Stock Index Cash Option, Underlying RIC: .STOXX50E
# instrument_3 = 'SPXj212240000.U'


datetime.now().isoformat(timespec='minutes')

'2023-03-29T14:48'


start = (timeOfCalcDatetime - pd.tseries.offsets.BDay(5)).strftime('%Y-%m-%dT%H:%M:%S.%f')  # '2022-10-05T07:30:00.000'
endDateTime = datetime.now()
end = endDateTime.strftime('%Y-%m-%dT%H:%M:%S.%f')  #  e.g.: '2022-09-09T20:00:00.000'
end

'2023-03-29T14:48:15.430844'


_RefDailyImpVolDf = historical_pricing.events.Definition(
    instrument, fields=['IMP_VOLT'], count=2000).get_data()


_RefDailyImpVolDf.data.df.head()


try: RefDailyImpVolDf = _RefDailyImpVolDf.data.df.drop(['EVENT_TYPE'], axis=1)  # In codebook, this line is needed
except: RefDailyImpVolDf = _RefDailyImpVolDf.data.df # If outside of codebook
fig = px.line(RefDailyImpVolDf, title = RefDailyImpVolDf.columns.name + " " + RefDailyImpVolDf.columns[0]) # This is just to see the implied vol graph when that field is available
fig.show()


# rd.get_history(
#     universe=["STXE35500J2.EX"],
#     fields=["TRDPRC_1"],
#     interval="tick")


_optnMrktPrice = rd.get_history(
    universe=[instrument],
    fields=["TRDPRC_1"],
    interval="10min",
    start=start,  # Ought to always start at 4 am for OPRA exchanged Options, more info in the article below
    end=end)  # Ought to always end at 8 pm for OPRA exchanged Options, more info in the article below

[Error 400 - invalid_grant] empty error description


_optnMrktPrice.head()


optnMrktPrice = _optnMrktPrice.resample('10Min').mean() # get a datapoint every 10 min
optnMrktPrice = optnMrktPrice[optnMrktPrice.index.strftime('%Y-%m-%d').isin([i for i in _optnMrktPrice.index.strftime('%Y-%m-%d').unique()])]  # Only keep trading days
optnMrktPrice = optnMrktPrice.loc[(optnMrktPrice.index.strftime('%H:%M:%S') >= '07:30:00') & (optnMrktPrice.index.strftime('%H:%M:%S') <= '22:00:00')]  # Only keep trading hours
optnMrktPrice.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values
print(f"Our dataframe started at {str(optnMrktPrice.index[0])} and went on continuously till {str(optnMrktPrice.index[-1])}, so out of trading hours rows are removed")
optnMrktPrice

Our dataframe started at 2023-03-23 10:10:00 and went on continuously till 2023-03-29 11:30:00, so out of trading hours rows are removed


# optnMrktPrice = optnMrktPrice.append(
#     pd.DataFrame(
#         [[pd.NA]], columns=optnMrktPrice.columns,
#         index=[(endDateTime + (datetime.min - endDateTime) % timedelta(minutes=10))]))
# optnMrktPrice


AToptnMrktPrice = _optnMrktPrice
AToptnMrktPrice


underlying = searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]].UnderlyingQuoteRIC.values[0][0]
underlying

'.STOXX50E'


hoursDf = rd.get_data(
    universe=["EUREX21"],
    fields=["ROW80_10"])
display(hoursDf)
hoursDf.iloc[0,1]

'       OGBL/OGBM/OGBS     07:30-08:00     08:00-19:00     19:00-20:00           '


_underlyingMrktPrice = rd.get_history(
    universe=[underlying],
    fields=["TRDPRC_1"],
    interval="10min",
    start=start,
    end=end)


_underlyingMrktPrice


ATunderlyingMrktPrice = AToptnMrktPrice.join(
    _underlyingMrktPrice, lsuffix='_OptPr', rsuffix='_UnderlyingPr', how='inner')
ATunderlyingMrktPrice.head(2)


if optnMrktPrice.index[-1] >= _underlyingMrktPrice.index[-1]:
    df = optnMrktPrice.copy()
    df['underlying ' + underlying + ' TRDPRC_1'] = _underlyingMrktPrice
else:
    df = _underlyingMrktPrice.copy()
    df.rename(columns={"TRDPRC_1": 'underlying ' + underlying + ' TRDPRC_1'}, inplace=True)
    df['TRDPRC_1'] = optnMrktPrice
    df.columns.name = optnMrktPrice.columns.name
df.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values
df = df.dropna()
df


strikePrice = searchDf2.iloc[
    (searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]].StrikePrice.values[0]


strikePrice

4225


_EurRfRate = rd.get_history(
    universe=['EURIBOR3MD='],  # USD3MFSR=, USDSOFR=
    fields=['TR.FIXINGVALUE'],
    # Since we will use `dropna()` as a way to select the rows we are after later on in the code, we need to ask for more risk-free data than needed, just in case we don't have enough:
    start=(datetime.strptime(start, '%Y-%m-%dT%H:%M:%S.%f') - timedelta(days=1)).strftime('%Y-%m-%d'),
    end=(datetime.strptime(end, '%Y-%m-%dT%H:%M:%S.%f') + timedelta(days=1)).strftime('%Y-%m-%d'))


_EurRfRate


EurRfRate = _EurRfRate.resample('10Min').mean().fillna(method='ffill')
df['EurRfRate'] = EurRfRate


df = df.fillna(method='ffill')
df


pd.options.mode.chained_assignment = None  # default='warn'
ATunderlyingMrktPrice['EurRfRate'] = [pd.NA for i in ATunderlyingMrktPrice.index]
for i in _EurRfRate.index:
    _i = str(i)[:10]
    for n, j in enumerate(ATunderlyingMrktPrice.index):
        if _i in str(j):
            if len(_EurRfRate.loc[i].values) == 2:
                ATunderlyingMrktPrice['EurRfRate'].iloc[n] = _EurRfRate.loc[i].values[0][0]
            elif len(_EurRfRate.loc[i].values) == 1:
                ATunderlyingMrktPrice['EurRfRate'].iloc[n] = _EurRfRate.loc[i].values[0]
ATdf = ATunderlyingMrktPrice.copy()


ATdf = ATdf.fillna(method='ffill')
ATdf.head(2)


dfGMT = df.copy()
dfLocalTimeZone = df.copy()
dfLocalTimeZone.index = [
    df.index[i].replace(
        tzinfo=pytz.timezone(
            'GMT')).astimezone(
        tz=datetime.now().astimezone().tzinfo)
    for i in range(len(df))]


dfGMT


dfLocalTimeZone


requestFields = [
    "MarketValueInDealCcy", "RiskFreeRatePercent",
    "UnderlyingPrice", "PricingModelType",
    "DividendType",
    "UnderlyingTimeStamp", "ReportCcy",
    "VolatilityType", "Volatility",
    "DeltaPercent", "GammaPercent",
    "RhoPercent", "ThetaPercent",
    "VegaPercent"]


universeL = [
        {
          "instrumentType": "Option",
          "instrumentDefinition": {
            "buySell": "Buy",
            "underlyingType": "Eti",
            "instrumentCode": instrument,
            "strike": str(strikePrice),
          },
          "pricingParameters": {
            "marketValueInDealCcy": str(dfLocalTimeZone['TRDPRC_1'][i]),
            "riskFreeRatePercent": str(dfLocalTimeZone['EurRfRate'][i]),
            "underlyingPrice": str(dfLocalTimeZone['underlying ' + underlying + ' TRDPRC_1'][i]),
            "pricingModelType": "BlackScholes",
            "dividendType": "ImpliedYield",
            "volatilityType": "Implied",
            "underlyingTimeStamp": "Default",
            "reportCcy": "EUR"
          }
        }
      for i in range(len(dfLocalTimeZone.index))]


def Chunks(lst, n):
    """Yield successive n-sized chunks from lst."""
    for i in range(0, len(lst), n):
        yield lst[i:i + n]


batchOf = 100
for i, j in enumerate(Chunks(universeL, batchOf)):
    print(f"Batch of {batchOf} requests no. {str(i+1)}/{str(len([i for i in Chunks(universeL, batchOf)]))} started")
    # Example request with Body Parameter - Symbology Lookup
    request_definition = rd.delivery.endpoint_request.Definition(
        method=rd.delivery.endpoint_request.RequestMethod.POST,
        url='https://api.refinitiv.com/data/quantitative-analytics/v1/financial-contracts',
        body_parameters={"fields": requestFields,
                         "outputs": ["Data", "Headers"],
                         "universe": j})

    response3 = request_definition.get_data()
    headers_name = [h['name'] for h in response3.data.raw['headers']]

    if i == 0:
        response3df = pd.DataFrame(
            data=response3.data.raw['data'], columns=headers_name)
    else:
        _response3df = pd.DataFrame(
            data=response3.data.raw['data'], columns=headers_name)
        response3df = response3df.append(_response3df, ignore_index=True)
    # display(_response3df)
    print(f"Batch of {batchOf} requests no. {str(i+1)}/{str(len([i for i in Chunks(universeL, batchOf)]))} ended")

Batch of 100 requests no. 1/3 started
Batch of 100 requests no. 1/3 ended
Batch of 100 requests no. 2/3 started
Batch of 100 requests no. 2/3 ended
Batch of 100 requests no. 3/3 started
Batch of 100 requests no. 3/3 ended


response3df


dfLocalTimeZone


CuniverseL = [  # C here is for the fact that we're using the content layer
    option.Definition(
        underlying_type=option.UnderlyingType.ETI,
        buy_sell='Buy',
        instrument_code=instrument,
        strike=float(strikePrice),
        pricing_parameters=option.PricingParameters(
            market_value_in_deal_ccy=float(dfLocalTimeZone['TRDPRC_1'][i]),
            risk_free_rate_percent=float(dfLocalTimeZone['EurRfRate'][i]),
            underlying_price=float(dfLocalTimeZone[
                'underlying ' + underlying + ' TRDPRC_1'][i]),
            pricing_model_type='BlackScholes',
            volatility_type='Implied',
            underlying_time_stamp='Default',
            report_ccy='EUR'))
    for i in range(len(dfLocalTimeZone.index))]


batchOf = 100
for i, j in enumerate(Chunks(CuniverseL, batchOf)):
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(CuniverseL, 100)])} started")
    # Example request with Body Parameter - Symbology Lookup
    response4 = rdf.Definitions(universe=j, fields=requestFields)
    response4 = response4.get_data()
    if i == 0:
        response4df = response4.data.df
    else:
        response4df = response4df.append(response4.data.df, ignore_index=True)
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(CuniverseL, 100)])} ended")

Batch of 100 requests no. 1/3 started
Batch of 100 requests no. 1/3 ended
Batch of 100 requests no. 2/3 started
Batch of 100 requests no. 2/3 ended
Batch of 34 requests no. 3/3 started
Batch of 34 requests no. 3/3 ended


IPADf = response4df.copy()  # IPA here stands for the service we used to get all the calculated valuse, Instrument Pricint Analitycs.
IPADf.index = dfLocalTimeZone.index
IPADf.columns.name = dfLocalTimeZone.columns.name
IPADf.rename(columns={"Volatility": 'ImpliedVolatility'}, inplace=True)
IPADf


ATdfGMT = ATdf.copy()
ATdfLocalTimeZone = ATdf.copy()
ATdfLocalTimeZone.index = [
    ATdf.index[i].replace(
        tzinfo=pytz.timezone(
            'GMT')).astimezone(
        tz=datetime.now().astimezone().tzinfo)
    for i in range(len(ATdf))]
ATdfGMT


ATdfLocalTimeZone


ATuniverseL = [
        {
          "instrumentType": "Option",
          "instrumentDefinition": {
            "buySell": "Buy",
            "underlyingType": "Eti",
            "instrumentCode": instrument,
            "strike": str(strikePrice),
          },
          "pricingParameters": {
            "marketValueInDealCcy": str(ATdfLocalTimeZone['TRDPRC_1_OptPr'][i]),
            "riskFreeRatePercent": str(ATdfLocalTimeZone['EurRfRate'][i]),
            "underlyingPrice": str(ATdfLocalTimeZone['TRDPRC_1_UnderlyingPr'][i]),
            "pricingModelType": "BlackScholes",
            "dividendType": "ImpliedYield",
            "volatilityType": "Implied",
            "underlyingTimeStamp": "Default",
            "reportCcy": "EUR"
          }
        }
      for i in range(len(ATdfLocalTimeZone.index))]


ATCUniverseL = [  # C here is for the fact that we're using the content layer
    option.Definition(
        underlying_type=option.UnderlyingType.ETI,
        buy_sell='Buy',
        instrument_code=instrument,
        strike=float(strikePrice),
        pricing_parameters=option.PricingParameters(
            market_value_in_deal_ccy=float(ATdfLocalTimeZone['TRDPRC_1_OptPr'][i]),
            risk_free_rate_percent=float(ATdfLocalTimeZone['EurRfRate'][i]),
            underlying_price=float(ATdfLocalTimeZone['TRDPRC_1_UnderlyingPr'][i]),
            pricing_model_type='BlackScholes',
            volatility_type='Implied',
            underlying_time_stamp='Default',
            report_ccy='EUR'))
    for i in range(len(ATdfLocalTimeZone.index))]


batchOf = 100
for i, j in enumerate(Chunks(ATCUniverseL, batchOf)):
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(ATCUniverseL, batchOf)])} started")
    # Example request with Body Parameter - Symbology Lookup
    response5 = rdf.Definitions(
        universe=j,
        fields=requestFields)
    response5 = response5.get_data()
    if i == 0:
        response5df = response5.data.df
    else:
        response5df = response5df.append(response5.data.df, ignore_index=True)
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(ATCUniverseL, batchOf)])} ended")

Batch of 48 requests no. 1/1 started
Batch of 48 requests no. 1/1 ended


response5df.head(2)


ATIPADf = response5df.copy()  # IPA here stands for the service we used to get all the calculated valuse, Instrument Pricint Analitycs.
ATIPADf.index = ATdfLocalTimeZone.index
ATIPADf.columns.name = ATdfLocalTimeZone.columns.name
ATIPADf.rename(columns={"Volatility": 'ImpliedVolatility'}, inplace=True)
ATIPADf.head(2)


display(searchDf2.iloc[
    (searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]])

IPADfGraph = IPADf[['ImpliedVolatility', 'MarketValueInDealCcy',
                    'RiskFreeRatePercent', 'UnderlyingPrice', 'DeltaPercent',
                    'GammaPercent', 'RhoPercent', 'ThetaPercent', 'VegaPercent']]

fig = px.line(IPADfGraph)  # This is just to see the implied vol graph when that field is available
# fig.layout = dict(xaxis=dict(type="category"))

# Format Graph: https://plotly.com/python/tick-formatting/
fig.update_layout(
    title=instrument,
    template='plotly_dark')

# Make it so that only one line is shown by default: # https://stackoverflow.com/questions/73384807/plotly-express-plot-subset-of-dataframe-columns-by-default-and-the-rest-as-opt
fig.for_each_trace(
    lambda t: t.update(
        visible=True if t.name in IPADfGraph.columns[:1] else "legendonly"))

# fig.update_xaxes(autorange=True)
# fig.update_layout(yaxis=IPADf.index[0::10])

fig.show()


fig = subplots.make_subplots(rows=3, cols=1)

fig.add_trace(go.Scatter(x=IPADf.index, y=IPADf.ImpliedVolatility, name='Op Imp Volatility'), row=1, col=1)
fig.add_trace(go.Scatter(x=IPADf.index, y=IPADf.MarketValueInDealCcy, name='Op Mk Pr'), row=2, col=1)
fig.add_trace(go.Scatter(x=IPADf.index, y=IPADf.UnderlyingPrice, name=underlying + ' Undrlyg Pr'), row=3, col=1)


fig.update(layout_xaxis_rangeslider_visible=False)
fig.update_layout(title=IPADf.columns.name)
fig.update_layout(
    template='plotly_dark',
    autosize=False,
    width=1300,
    height=500)
fig.show()
searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]]


display(searchDf2.iloc[(searchDf2.StrikePrice-currentUnderlyingPrc).abs().argsort()[:1]])
ATIPADfSimpleGraph = pd.DataFrame(
    data=ATIPADf.ImpliedVolatility.values, index=ATIPADf.ImpliedVolatility.index)

fig, ax = plt.subplots(ncols=1)

ax.plot(ATIPADfSimpleGraph, '.-')
# ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
ax.set_title(f"{searchDf2.iloc[(searchDf2.StrikePrice-currentUnderlyingPrc).abs().argsort()[:1]].RIC.values[0]} Implied Volatility At Trade Only")
fig.autofmt_xdate()

plt.show()


def ImpVolatilityCalcIPA(x=15,
                         instrument=None,
                         indexUnderlying=".STOXX50E",
                         callOrPut='Put',
                         dateBack=3,
                         expiryYearOfInterest=datetime.now().year,
                         riskFreeRate=None, riskFreeRateField=None,
                         timeZoneInGraph=datetime.now().astimezone(),
                         maxColwidth=200,
                         graphStyle='overlay',  # 'overlay', '3 graphs', 'simple'
                         simpleGraphLineStyle='.-',  # 'o-'
                         simpleGraphSize=(15, 5),
                         graphTemplate='plotly_dark',
                         debug=False,
                         returnDfGraph=False,
                         AtOptionTradeOnly=False):


    if indexUnderlying == ".STOXX50E":
        exchangeC, exchangeRIC, mcalGetCalendar = 'EUX', 'STX', 'EUREX'
    elif indexUnderlying == '.SPX':
        exchangeC, exchangeRIC, mcalGetCalendar = 'OPQ', 'SPX', 'CBOE_Futures'  # 'CBOE_Index_Options'  # should be 'CBOE_Index_Options'... CBOT_Equity


    def Get_exp_dates(year=expiryYearOfInterest,
                      days=True,
                      mcal_get_calendar=mcalGetCalendar):
        '''
        Get_exp_dates Version 3.0:

        This function gets expiration dates for a year for NDX options, which are the 3rd Fridays of each month.

        Changes
        ----------------------------------------------
        Changed from Version 1.0 to 2.0: Jonathan Legrand changed Haykaz Aramyan's original code to allow
            (i) for the function's holiday argument to be changed, and defaulted to 'EUREX' as opposed to 'CBOE_Index_Options' and
            (ii) for the function to output full date objects as opposed to just days of the month if agument days=True.

        Changed from Version 2.0 to 3.0: Jonathan Legrand changed this function to reflec tthe fact that it can be used for indexes other than EUREX.

        Dependencies
        ----------------------------------------------
        Python library 'pandas_market_calendars' version 3.2

        Parameters
        -----------------------------------------------
        Input:
            year(int): year for which expiration days are requested

            mcal_get_calendar(str):
                String of the calendar for which holidays have to be taken into account.
                More on this calendar (link to Github checked 2022-10-11): https://github.com/rsheftel/pandas_market_calendars/blob/177e7922c7df5ad249b0d066b5c9e730a3ee8596/pandas_market_calendars/exchange_calendar_cboe.py
                Default: mcal_get_calendar='EUREX'

            days(bool): If True, only days of the month is outputed, else it's dataeime objects
                Default: days=True

        Output:
            dates(dict): dictionary of expiration days for each month of a specified year in datetime.date format.
        '''

        # get CBOE market holidays
        Cal = mcal.get_calendar(mcal_get_calendar)
        holidays = Cal.holidays().holidays

        # set calendar starting from Saturday
        c = calendar.Calendar(firstweekday=calendar.SATURDAY)

        # get the 3rd Friday of each month
        exp_dates = {}
        for i in range(1, 13):
            monthcal = c.monthdatescalendar(year, i)
            date = monthcal[2][-1]
            # check if found date is an holiday and get the previous date if it is
            if date in holidays:
                date = date + timedelta(-1)
            # append the date to the dictionary
            if year in exp_dates:
                ### Changed from original code from here on by Jonathan Legrand on 2022-10-11
                if days: exp_dates[year].append(date.day)
                else: exp_dates[year].append(date)
            else:
                if days: exp_dates[year] = [date.day]
                else: exp_dates[year] = [date]
        return exp_dates

    timeOfCalcDatetime = datetime.now()  # For now, we will focuss on the use-case where we are calculating values for today; later we will allow for it historically for any day going back a few business days.
    timeOfCalcStr = datetime.now().strftime('%Y-%m-%d')
    fullDatesAtTimeOfCalc = Get_exp_dates(timeOfCalcDatetime.year, days=False)  # `timeOfCalcDatetime.year` here is 2023
    fullDatesAtTimeOfCalcDatetime = [
        datetime(i.year, i.month, i.day)
        for i in fullDatesAtTimeOfCalc[list(fullDatesAtTimeOfCalc.keys())[0]]]
    expiryDateOfInt = [i for i in fullDatesAtTimeOfCalcDatetime
                       if i > timeOfCalcDatetime + relativedelta(days=x)][0]

    if debug: print(f"expiryDateOfInt: {expiryDateOfInt}")

    response = search.Definition(
        view = search.Views.SEARCH_ALL, # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`
        query=indexUnderlying,
        select="DocumentTitle, RIC, StrikePrice, ExchangeCode, ExpiryDate, UnderlyingAsset, " +
                "UnderlyingAssetName, UnderlyingAssetRIC, ESMAUnderlyingIndexCode, RCSUnderlyingMarket" +
                "UnderlyingQuoteName, UnderlyingQuoteRIC, InsertDateTime, RetireDate",
        filter=f"RCSAssetCategoryLeaf eq 'Option' and RIC eq '{exchangeRIC}*' and DocumentTitle ne '*Weekly*' " +
        f"and CallPutOption eq '{callOrPut}' and ExchangeCode eq '{exchangeC}' and " +
        f"ExpiryDate ge {(expiryDateOfInt - relativedelta(days=1)).strftime('%Y-%m-%d')} " +
        f"and ExpiryDate lt {(expiryDateOfInt + relativedelta(days=1)).strftime('%Y-%m-%d')}",  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.
        top=10000,
    ).get_data()
    searchDf = response.data.df

    if debug: display(searchDf)

    try:
        underlyingPrice =  rd.get_history(
            universe=[indexUnderlying],
            fields=["TRDPRC_1"],
            interval="tick").iloc[-1][0]
    except:
        print("Function failed at the search strage, returning the following dataframe: ")
        display(searchDf)

    if debug:
        print(f"Underlying {indexUnderlying}'s price recoprded here was {underlyingPrice}")
        display(searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:10]])

    if instrument is None:
        instrument = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:1]].RIC.values[0]

    start = (timeOfCalcDatetime - pd.tseries.offsets.BDay(dateBack)).strftime('%Y-%m-%dT%H:%M:%S.%f')  # '2022-10-05T07:30:00.000'
    endDateTime = datetime.now()
    end = endDateTime.strftime('%Y-%m-%dT%H:%M:%S.%f')  #  e.g.: '2022-09-09T20:00:00.000'

    _optnMrktPrice = rd.get_history(
        universe=[instrument],
        fields=["TRDPRC_1"],
        interval="10min",
        start=start,  # Ought to always start at 4 am for OPRA exchanged Options, more info in the article below
        end=end)  # Ought to always end at 8 pm for OPRA exchanged Options, more info in the article below

    if debug:
        print(instrument)
        display(_optnMrktPrice)

    ## Data on certain options are stale and do not nessesarily show up on Workspace, in case that happens, we will pick the next ATM Option, which probably will have the same strike, but we will only do so once, any more and we could get too far from strike:
    if _optnMrktPrice.empty:
        if debug: print(f"No data could be found for {instrument}, so the next ATM Option was chosen")
        instrument = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[1:2]].RIC.values[0]
        if debug: print(f"{instrument}")
        _optnMrktPrice = rd.get_history(universe=[instrument],
                                        fields=["TRDPRC_1"], interval="10min",
                                        start=start, end=end)
        if debug: display(_optnMrktPrice)
    if _optnMrktPrice.empty:  # Let's try one more time, as is often nessesary
        if debug: print(f"No data could be found for {instrument}, so the next ATM Option was chosen")
        instrument = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[2:3]].RIC.values[0]
        if debug: print(f"{instrument}")
        _optnMrktPrice = rd.get_history(universe=[instrument],
                                        fields=["TRDPRC_1"], interval="10min",
                                        start=start, end=end)
        if debug: display(_optnMrktPrice)
    if _optnMrktPrice.empty:
        print(f"No data could be found for {instrument}, please check it on Refinitiv Workspace")

    optnMrktPrice = _optnMrktPrice.resample('10Min').mean() # get a datapoint every 10 min
    optnMrktPrice = optnMrktPrice[optnMrktPrice.index.strftime('%Y-%m-%d').isin([i for i in _optnMrktPrice.index.strftime('%Y-%m-%d').unique()])]  # Only keep trading days
    optnMrktPrice = optnMrktPrice.loc[(optnMrktPrice.index.strftime('%H:%M:%S') >= '07:30:00') & (optnMrktPrice.index.strftime('%H:%M:%S') <= '22:00:00')]  # Only keep trading hours
    optnMrktPrice.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values

    # Note also that one may want to only look at 'At Option Trade' datapoints,
    # i.e.: Implied Volatility when a trade is made for the Option, but not when
    # none is made. For this, we will use the 'At Trade' (`AT`) dataframes:
    if AtOptionTradeOnly: AToptnMrktPrice = _optnMrktPrice

    underlying = searchDf.iloc[(searchDf.StrikePrice).abs().argsort()[:1]].UnderlyingQuoteRIC.values[0][0]

    _underlyingMrktPrice = rd.get_history(
        universe=[underlying],
        fields=["TRDPRC_1"],
        interval="10min",
        start=start,
        end=end)
    # Let's put it al in one data-frame, `df`. Some datasets will have data
    # going from the time we sert for `start` all the way to `end`. Some won't
    # because no trade happened in the past few minutes/hours. We ought to base
    # ourselves on the dataset with values getting closer to `end` and `ffill`
    # for the other column. As a result, the following `if` loop is needed:
    if optnMrktPrice.index[-1] >= _underlyingMrktPrice.index[-1]:
        df = optnMrktPrice.copy()
        df[f"underlying {underlying} TRDPRC_1"] = _underlyingMrktPrice
    else:
        df = _underlyingMrktPrice.copy()
        df.rename(
            columns={"TRDPRC_1": f"underlying {underlying} TRDPRC_1"},
            inplace=True)
        df['TRDPRC_1'] = optnMrktPrice
        df.columns.name = optnMrktPrice.columns.name
    df.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values
    df = df.dropna()

    if AtOptionTradeOnly:
        ATunderlyingMrktPrice = AToptnMrktPrice.join(
            _underlyingMrktPrice, lsuffix='_OptPr', how='inner',
            rsuffix=f" Underlying {underlying} TRDPRC_1")
        ATdf = ATunderlyingMrktPrice

    strikePrice = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:1]].StrikePrice.values[0]

    if riskFreeRate is None and indexUnderlying == ".SPX":
        _riskFreeRate = 'USDCFCFCTSA3M='
        _riskFreeRateField = 'TR.FIXINGVALUE'
    elif riskFreeRate is None and indexUnderlying == ".STOXX50E":
        _riskFreeRate = 'EURIBOR3MD='
        _riskFreeRateField = 'TR.FIXINGVALUE'
    else:
        _riskFreeRate, _riskFreeRateField = riskFreeRate, riskFreeRateField

    _RfRate = rd.get_history(
        universe=[_riskFreeRate],  # USD3MFSR=, USDSOFR=
        fields=[_riskFreeRateField],
        # Since we will use `dropna()` as a way to select the rows we are after later on in the code, we need to ask for more risk-free data than needed, just in case we don't have enough:
        start=(datetime.strptime(start, '%Y-%m-%dT%H:%M:%S.%f') - timedelta(days=1)).strftime('%Y-%m-%d'),
        end=(datetime.strptime(end, '%Y-%m-%dT%H:%M:%S.%f') + timedelta(days=1)).strftime('%Y-%m-%d'))
    RfRate = _RfRate.resample('10Min').mean().fillna(method='ffill')
    if AtOptionTradeOnly:
        pd.options.mode.chained_assignment = None  # default='warn'
        ATunderlyingMrktPrice['RfRate'] = [pd.NA for i in ATunderlyingMrktPrice.index]
        for i in RfRate.index:
            _i = str(i)[:10]
            for n, j in enumerate(ATunderlyingMrktPrice.index):
                if _i in str(j):
                    if len(RfRate.loc[i].values) == 2:
                        ATunderlyingMrktPrice['RfRate'].iloc[n] = RfRate.loc[i].values[0][0]
                    elif len(RfRate.loc[i].values) == 1:
                        ATunderlyingMrktPrice['RfRate'].iloc[n] = RfRate.loc[i].values[0]
        ATdf = ATunderlyingMrktPrice.copy()
        ATdf = ATdf.fillna(method='ffill')  # This is in case there were no Risk Free datapoints released after a certain time, but trades on the option still went through.
    else:
        df['RfRate'] = RfRate
        df = df.fillna(method='ffill')

    if timeZoneInGraph != 'GMT' and AtOptionTradeOnly:
        ATdf.index = [
            ATdf.index[i].replace(
                tzinfo=pytz.timezone(
                    'GMT')).astimezone(
                tz=datetime.now().astimezone().tzinfo)
            for i in range(len(ATdf))]
    elif timeZoneInGraph != 'GMT':
        df.index = [
            df.index[i].replace(
                tzinfo=pytz.timezone(
                    'GMT')).astimezone(
                tz=timeZoneInGraph.tzinfo)
            for i in range(len(df))]

    if AtOptionTradeOnly:
        if debug:
            print("ATdf")
            display(ATdf)
        universeL = [
            option.Definition(
                underlying_type=option.UnderlyingType.ETI,
                buy_sell='Buy',
                instrument_code=instrument,
                strike=float(strikePrice),
                pricing_parameters=option.PricingParameters(
                    market_value_in_deal_ccy=float(ATdf.TRDPRC_1_OptPr[i]),
                    risk_free_rate_percent=float(ATdf.RfRate[i]),
                    underlying_price=float(ATdf[
                        f"TRDPRC_1 Underlying {underlying} TRDPRC_1"][i]),
                    pricing_model_type='BlackScholes',
                    volatility_type='Implied',
                    underlying_time_stamp='Default',
                    report_ccy='EUR'))
            for i in range(len(ATdf.index))]
    else:
        if debug:
            print("df")
            display(df)
        universeL = [  # C here is for the fact that we're using the content layer
            option.Definition(
                underlying_type=option.UnderlyingType.ETI,
                buy_sell='Buy',
                instrument_code=instrument,
                strike=float(strikePrice),
                pricing_parameters=option.PricingParameters(
                    market_value_in_deal_ccy=float(df.TRDPRC_1[i]),
                    risk_free_rate_percent=float(df.RfRate[i]),
                    underlying_price=float(df[
                        f"underlying {underlying} TRDPRC_1"][i]),
                    pricing_model_type='BlackScholes',
                    volatility_type='Implied',
                    underlying_time_stamp='Default',
                    report_ccy='EUR'))
            for i in range(len(df.index))]

    def Chunks(lst, n):
        """Yield successive n-sized chunks from lst."""
        for i in range(0, len(lst), n):
            yield lst[i:i + n]

    requestFields = [
        "MarketValueInDealCcy", "RiskFreeRatePercent",
        "UnderlyingPrice", "PricingModelType",
        "DividendType", "UnderlyingTimeStamp",
        "ReportCcy", "VolatilityType",
        "Volatility", "DeltaPercent", "GammaPercent",
        "RhoPercent", "ThetaPercent", "VegaPercent"]

    batchOf = 100
    for i, j in enumerate(Chunks(universeL, batchOf)):
        if debug: print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(universeL, batchOf)])} started")
        # Example request with Body Parameter - Symbology Lookup
        request_definition = rdf.Definitions(universe=j, fields=requestFields)
        response = request_definition.get_data()
        if i == 0:
            IPADf = response.data.df
        else:
            IPADf = IPADf.append(response.data.df, ignore_index=True)
        if debug: print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(universeL, batchOf)])} ended")

    if AtOptionTradeOnly:
        IPADf.index = ATdf.index
        IPADf.columns.name = ATdf.columns.name
    else:
        IPADf.index = df.index
        IPADf.columns.name = df.columns.name
    IPADf.rename(columns={"Volatility": 'ImpliedVolatility'}, inplace=True)

    # We are going to want to show details about data retreived in a dataframe in the output of this function. The one line below allows us to maximise the width (column) length of cells to see all that is written within them.
    pd.options.display.max_colwidth = maxColwidth

    if graphStyle == 'simple':
        display(searchDf.iloc[(searchDf.StrikePrice-underlyingPrice).abs().argsort()[:1]])
        fig, axes = plt.subplots(ncols=1, figsize=simpleGraphSize)
        axes.plot(
            pd.DataFrame(  # Unfortunutally, Matplotlib, which is the library used here for simple graphs, require our dataframe to be in a specific format that necessitate the use of `pd.DataFrame`
                data=IPADf[['ImpliedVolatility']].ImpliedVolatility.values,
                index=IPADf[['ImpliedVolatility']].ImpliedVolatility.index),
            simpleGraphLineStyle)
        if AtOptionTradeOnly: axes.set_title(f"{instrument} Implied Volatility At Trade Only")
        else: axes.set_title(f"{instrument} Implied Volatility")
        plt.show()

    else:

        display(searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:1]])

        IPADfGraph = IPADf[
            ['ImpliedVolatility', 'MarketValueInDealCcy',
             'RiskFreeRatePercent', 'UnderlyingPrice', 'DeltaPercent',
             'GammaPercent', 'RhoPercent', 'ThetaPercent', 'VegaPercent']]

        if debug: display(IPADfGraph)

        try:  # This is needed in case there is not enough data to calculate values for all timestamps , see https://stackoverflow.com/questions/67244912/wide-format-csv-with-plotly-express
            fig = px.line(IPADfGraph)
        except:
            if returnDfGraph:
                return IPADfGraph
            else:
                IPADfGraph = IPADfGraph[
                    ["ImpliedVolatility", "MarketValueInDealCcy",
                     "RiskFreeRatePercent", "UnderlyingPrice"]]
                fig = px.line(IPADfGraph)

        if graphStyle == 'overlay':
            fig.update_layout(
                title=instrument,
                template=graphTemplate)
            fig.for_each_trace(
                lambda t: t.update(
                    visible=True if t.name in IPADfGraph.columns[:1] else "legendonly"))
            fig.show()

        elif graphStyle == '3 graphs':
            fig = plotly.subplots.make_subplots(rows=3, cols=1)

            fig.add_trace(go.Scatter(
                x=IPADf.index, y=IPADfGraph.ImpliedVolatility,
                name='Op Imp Volatility'), row=1, col=1)
            fig.add_trace(go.Scatter(
                x=IPADf.index, y=IPADfGraph.MarketValueInDealCcy,
                name='Op Mk Pr'), row=2, col=1)
            fig.add_trace(go.Scatter(
                x=IPADf.index, y=IPADfGraph.UnderlyingPrice,
                name=f"{underlying} Undrlyg Pr"), row=3, col=1)

            fig.update(layout_xaxis_rangeslider_visible=False)
            fig.update_layout(title=IPADfGraph.columns.name)
            fig.update_layout(
                title=instrument,
                template=graphTemplate,
                autosize=False,
                width=1300,
                height=500)
            fig.show()

        else:

            print("Looks like the agrument `graphStyle` used is incorrect. Try `simple`, `overlay` or `3 graphs`")


ImpVolatilityCalcIPA(  # This will pick up 10 min data
    x=15,
    indexUnderlying=".SPX",  # ".SPX" or ".STOXX50E"
    callOrPut='Call',  # 'Put' or 'Call'
    dateBack=3,
    expiryYearOfInterest=datetime.now().year,
    riskFreeRate=None,
    riskFreeRateField=None,  # 'TR.FIXINGVALUE'
    timeZoneInGraph=datetime.now().astimezone(),
    maxColwidth=200,
    graphStyle='overlay',  # 'overlay', '3 graphs', 'simple'
    simpleGraphLineStyle='.-',  # 'o-'
    simpleGraphSize=(15, 5),
    graphTemplate='plotly_dark',
    debug=False,
    returnDfGraph=True,
    AtOptionTradeOnly=True)


# while True:
#     # Code executed here
#     clear_output(wait=True)
#     ImpVolatilityCalcIPA(
#                 dateBack=3, indexUnderlying=".STOXX50E", callOrPut='Call',
#                 graphStyle='simple', AtOptionTradeOnly=True)
#             time.sleep(5)


timeOfCalc2, indexUnderlying2 = "2022-04-01", ".STOXX50E"
timeOfCalcDatetime2 = datetime.strptime(timeOfCalc2, '%Y-%m-%d')
currentUnderlyingPrc2 = rd.get_history(
    universe=[indexUnderlying2],
    start=timeOfCalc2,  # , end: "OptDateTime"=None
    fields=["TRDPRC_1"],
    interval="tick").iloc[-1][0]
currentUnderlyingPrc2

4214.51


if indexUnderlying2 == ".STOXX50E":
    exchangeC2, exchangeRIC2, mcalGetCalendar2 = "EUX", "STX", "EUREX"
elif indexUnderlying2 == ".SPX":
    exchangeC2, exchangeRIC2, mcalGetCalendar2 = "OPQ", "SPX", "CBOE_Futures"
exchangeC2, exchangeRIC2, mcalGetCalendar2

('EUX', 'STX', 'EUREX')


fullDatesAtTimeOfCalc2 = Get_exp_dates(
    year=2022, days=False,
    mcal_get_calendar=mcalGetCalendar2)
fullDatesAtTimeOfCalc2

{2022: [datetime.date(2022, 1, 21),
  datetime.date(2022, 2, 18),
  datetime.date(2022, 3, 18),
  datetime.date(2022, 4, 14),
  datetime.date(2022, 5, 20),
  datetime.date(2022, 6, 17),
  datetime.date(2022, 7, 15),
  datetime.date(2022, 8, 19),
  datetime.date(2022, 9, 16),
  datetime.date(2022, 10, 21),
  datetime.date(2022, 11, 18),
  datetime.date(2022, 12, 16)]}


fullDatesAtTimeOfCalcDatetime2 = [
    datetime(i.year, i.month, i.day)
    for i in fullDatesAtTimeOfCalc2[list(fullDatesAtTimeOfCalc2.keys())[0]]]
fullDatesAtTimeOfCalcDatetime2

[datetime.datetime(2022, 1, 21, 0, 0),
 datetime.datetime(2022, 2, 18, 0, 0),
 datetime.datetime(2022, 3, 18, 0, 0),
 datetime.datetime(2022, 4, 14, 0, 0),
 datetime.datetime(2022, 5, 20, 0, 0),
 datetime.datetime(2022, 6, 17, 0, 0),
 datetime.datetime(2022, 7, 15, 0, 0),
 datetime.datetime(2022, 8, 19, 0, 0),
 datetime.datetime(2022, 9, 16, 0, 0),
 datetime.datetime(2022, 10, 21, 0, 0),
 datetime.datetime(2022, 11, 18, 0, 0),
 datetime.datetime(2022, 12, 16, 0, 0)]


expiryDateOfInt2 = [i for i in fullDatesAtTimeOfCalcDatetime2
                    if i > timeOfCalcDatetime2 + relativedelta(days=x)][0]
expiryDateOfInt2

datetime.datetime(2022, 5, 20, 0, 0)


def Get_exp_month(exp_date, opt_type):

    # define option expiration identifiers
    ident = {
        '1':  {'exp': 'A', 'C': 'A', 'P': 'M'},
        '2':  {'exp': 'B', 'C': 'B', 'P': 'N'},
        '3':  {'exp': 'C', 'C': 'C', 'P': 'O'},
        '4':  {'exp': 'D', 'C': 'D', 'P': 'P'},
        '5':  {'exp': 'E', 'C': 'E', 'P': 'Q'},
        '6':  {'exp': 'F', 'C': 'F', 'P': 'R'},
        '7':  {'exp': 'G', 'C': 'G', 'P': 'S'},
        '8':  {'exp': 'H', 'C': 'H', 'P': 'T'},
        '9':  {'exp': 'I', 'C': 'I', 'P': 'U'},
        '10': {'exp': 'J', 'C': 'J', 'P': 'V'},
        '11': {'exp': 'K', 'C': 'K', 'P': 'W'},
        '12': {'exp': 'L', 'C': 'L', 'P': 'X'}}

    # get expiration month code for a month
    if opt_type.upper() == 'C':
        exp_month = ident[str(exp_date.month)]['C']
    elif opt_type.upper() == 'P':
        exp_month = ident[str(exp_date.month)]['P']

    return ident, exp_month


def Check_ric(ric, maturity, ident):
    exp_date = pd.Timestamp(maturity)

    # get start and end date for get_historical_price_summaries
    # query (take current date minus 90 days period)
    sdate = (datetime.now() - timedelta(90)).strftime('%Y-%m-%d')
    edate = datetime.now().strftime('%Y-%m-%d')

    # check if option is matured. If yes, add expiration syntax and recalculate
    # start and end date of the query (take expiration day minus 90 days period)
    if pd.Timestamp(maturity) < datetime.now():
        ric = ric + '^' + ident[str(exp_date.month)]['exp'] + str(exp_date.year)[-2:]
        sdate = (exp_date - timedelta(90)).strftime('%Y-%m-%d')
        edate = exp_date.strftime('%Y-%m-%d')

    # request option prices. Please note, there is no settle price for OPRA traded options
    fieldsRequest = ['BID', 'ASK', 'TRDPRC_1']
    if not ric.split('.')[1][0] == 'U':
        fieldsRequest.append('SETTLE')

    prices = rd.content.historical_pricing.summaries.Definition(
        ric, start=sdate, end=edate,
        interval=rd.content.historical_pricing.Intervals.DAILY,
        fields=fieldsRequest).get_data()

    return ric, prices


def Get_ric_eurex(asset, maturity, strike, opt_type):
    exp_date = pd.Timestamp(maturity)

    if asset[0] == '.':
        asset_name = asset[1:]
        if asset_name == 'FTSE':
            asset_name = 'OTUK'
        elif asset_name == 'SSMI':
            asset_name = 'OSMI'
        elif asset_name == 'GDAXI':
            asset_name = 'GDAX'
        elif asset_name == 'ATX':
            asset_name = 'FATXA'
        elif asset_name == 'STOXX50E':
            asset_name = 'STXE'
    else:
        asset_name = asset.split('.')[0]

    ident, exp_month = Get_exp_month(
        exp_date=exp_date, opt_type=opt_type)

    if type(strike) == float:
        int_part = int(strike)
        dec_part = str(str(strike).split('.')[1])[0]
    else:
        int_part = int(strike)
        dec_part = '0'

    if len(str(int(strike))) == 1:
        strike_ric = '0' + str(int_part) + dec_part
    else:
        strike_ric = str(int_part) + dec_part

    possible_rics = []
    generations = ['', 'a', 'b', 'c', 'd']
    for gen in generations:
        ric = asset_name + strike_ric + gen + exp_month + str(exp_date.year)[-1:] + '.EX'
        ric, prices = Check_ric(ric, maturity, ident)
        if prices is not None:
            return ric, prices
        else:
            possible_rics.append(ric)
    print(f'Here is a list of possible RICs {possible_rics}, however we could not find any prices for those!')
    return ric, prices


int(round(currentUnderlyingPrc2, -2))

4200


instrument2, instrument2Prices = Get_ric_eurex(
    asset='.STOXX50E', opt_type='P',
    maturity=expiryDateOfInt2.strftime('%Y-%m-%d'),
    strike=int(round(currentUnderlyingPrc2, -2)))


instrument2

'STXE42000Q2.EX^E22'


instrument2Prices.data.df


from typing import Tuple, Union, Dict, List, Any


class Option_RIC:
    """
    Option_RIC
    """

    def __init__(
        self,
        maturity: str,  # '2022-01-21'
        strike: int,
        opt_type: str,  # 'C' or 'P'
        asset: str = ".STOXX50E",
        debug: bool = False,
        topNuSearchResults: int = 100):

        # Most objects are simple to define at this stage, but soon you'll see that two of them are a little more finicky
        self.maturity = pd.Timestamp(maturity)
        self.strike = strike
        self.opt_type = opt_type
        self.debug = debug
        self.asset = asset

        response = search.Definition(
            query=asset,
            filter="SearchAllCategory eq 'Options' and Periodicity eq 'Monthly' ",
            select=' RIC, DocumentTitle, UnderlyingQuoteRIC,Periodicity, ExchangeCode',
            navigators="ExchangeCode",
            top=topNuSearchResults).get_data()
        result = response.data.raw["Navigators"]["ExchangeCode"]
        exchange_codes = []
        for i in range(len(result['Buckets'])):
            code = result['Buckets'][i]['Label']
            exchange_codes.append(code)

        self.exchange = exchange_codes


    def Check_ric(self, ric, maturity):
        """
        Support Function used within other functions in `Option_RIC` Class.
        """

        exp_date = pd.Timestamp(maturity)

        if pd.Timestamp(maturity) < datetime.now():
            sdate = (exp_date - timedelta(600)).strftime('%Y-%m-%d')
            edate = exp_date.strftime('%Y-%m-%d')
        else:
            sdate = (datetime.now() - timedelta(90)).strftime('%Y-%m-%d')
            edate = datetime.now().strftime('%Y-%m-%d')
        if self.debug:
            print(f"Check_ric's (ric, sdate, edate) = ({ric}, {sdate}, {edate})")

        # Now things are gettijng tricky.
        # Certain Expiered Options do not have 'TRDPRC_1' data historically. Some don't have 'SETTLE'. Some have both...
        # The below should capture 'SETTLE' when it is available, but 'TRDPRC_1' might still be present in these instances.
        # So we will need to build a logic to focus on the series with the most datapoints.
        if ric.split('.')[1][0] == 'U':
            prices = rd.content.historical_pricing.summaries.Definition(
                ric,  start=sdate, end=edate,
                interval=rd.content.historical_pricing.Intervals.DAILY,
                fields=['TRDPRC_1', 'BID', 'ASK']).get_data()  # Later in the code, we will pick the column that has the fewest `<NA>`s. It oculd be that 'BID' and 'TRDPRC_1' have just as many `<NA>`s; the code will pick the 1st column with the same number of `<NA>`s in this case, so we have to ask for fields in the order of importance. Here we're most interested in 'TRDPRC_1', the rest later.
        else:
            prices = rd.content.historical_pricing.summaries.Definition(
                ric,  start=sdate, end=edate,
                interval=rd.content.historical_pricing.Intervals.DAILY,
                fields=['SETTLE', 'TRDPRC_1', 'BID', 'ASK']).get_data()
        if self.debug:
            print(f"prices.data.df.isna().sum(axis=0).idxmin() = {prices.data.df.isna().sum(axis=0).idxmin()}")
            print(f"prices.data.df[prices.data.df.isna().sum(axis=0).idxmin()] = {prices.data.df[prices.data.df.isna().sum(axis=0).idxmin()]}")
        fullest_prices = pd.DataFrame(  # Deppending on which Option (and underlying) user picks, the apropriate price field changes - annoyingly. `fullest_prices` attempts to pick only the collumn - and thus the field - with fewest `NaN`s, which ought to be the correct field/column.
            columns=[prices.data.df.isna().sum(axis=0).idxmin()],  # This is the name of the column with fewest NAs
            data=prices.data.df[prices.data.df.isna().sum(axis=0).idxmin()])  # This is the column with fewest NAs
        return ric, prices, fullest_prices

    def Get_asset_and_exchange(self):
        """
        Support Function used within other functions in `Option_RIC` Class.
        """

        asset_in_ric: Dict[str, Dict[Union[str, Dict[str, str]]]] = {
            'SSMI':     {'EUX': 'OSMI'},
            'GDAXI':    {'EUX': 'GDAX'},
            'ATX':      {'EUX': 'FATXA'},
            'STOXX50E': {'EUX': 'STXE'},
            'FTSE':     {'IEU': 'LFE', 'EUX': 'OTUK'},
            'N225':     {'OSA': 'JNI'},
            'TOPX':     {'OSA': 'JTI'}}

        asset_exchange: Dict[str, str] = {}
        if self.asset[0] != '.':
            asset: str = self.asset.split('.')[0]
        else:
            asset: str = self.asset[1:]
        for exch in self.exchange:
            if asset in asset_in_ric:
                asset_exchange[exch] = asset_in_ric[asset][exch]
            else:
                asset_exchange[exch] = asset
        return asset_exchange

    def Get_strike(self, exch):

        if exch == 'OPQ':
            if type(self.strike) == float:
                int_part = int(self.strike)
                dec_part = str(str(self.strike).split('.')[1])
            else:
                int_part = int(self.strike)
                dec_part = '00'
            if int(self.strike) < 10:
                strike_ric = '00' + str(int_part) + dec_part
            elif int_part >= 10 and int_part < 100:
                strike_ric = '0' + str(int_part) + dec_part
            elif int_part >= 100 and int_part < 1000:
                strike_ric = str(int_part) + dec_part
            elif int_part >= 1000 and int_part < 10000:
                strike_ric = str(int_part) + '0'
            elif int_part >= 10000 and int_part < 20000:
                strike_ric = 'A' + str(int_part)[-4:]
            elif int_part >= 20000 and int_part < 30000:
                strike_ric = 'B' + str(int_part)[-4:]
            elif int_part >= 30000 and int_part < 40000:
                strike_ric = 'C' + str(int_part)[-4:]
            elif int_part >= 40000 and int_part < 50000:
                strike_ric = 'D' + str(int_part)[-4:]

        elif exch == 'HKG' or exch == 'HFE':
            if self.asset[0] == '.':
                strike_ric = str(int(self.strike))
            else:
                strike_ric = str(int(self.strike * 100))

        elif exch == 'OSA':
            strike_ric = str(self.strike)[:3]

        elif exch == 'EUX' or exch == 'IEU':
            if type(self.strike) == float and len(str(int(self.strike))) == 1:
                int_part = int(self.strike)
                dec_part = str(str(self.strike).split('.')[1])[0]
                strike_ric = '0' + str(int_part) + dec_part
            elif (len(str(int(self.strike))) > 1 and exch == 'EUX'):
                strike_ric = str(int(self.strike)) + '0'
            elif (len(str(int(self.strike))) == 2 and exch == 'IEU'):
                strike_ric = '0' + str(int(self.strike))
            elif len(str(int(self.strike))) > 2 and exch == 'IEU':
                strike_ric = str(int(self.strike))

        return strike_ric

    def Get_exp_month(self, exchange):
        """
        Support Function used within other functions in `Option_RIC` Class.
        """
        ident_opra = {
            '1': {'exp': 'A', 'C_bigStrike': 'a', 'C_smallStrike': 'A',
                  'P_bigStrike': 'm', 'P_smallStrike': 'M'},
            '2': {'exp': 'B', 'C_bigStrike': 'b', 'C_smallStrike': 'B',
                  'P_bigStrike': 'n', 'P_smallStrike': 'N'},
            '3': {'exp': 'C', 'C_bigStrike': 'c', 'C_smallStrike': 'C',
                  'P_bigStrike': 'o', 'P_smallStrike': 'O'},
            '4': {'exp': 'D', 'C_bigStrike': 'd', 'C_smallStrike': 'D',
                  'P_bigStrike': 'p', 'P_smallStrike': 'P'},
            '5': {'exp': 'E', 'C_bigStrike': 'e', 'C_smallStrike': 'E',
                  'P_bigStrike': 'q', 'P_smallStrike': 'Q'},
            '6': {'exp': 'F', 'C_bigStrike': 'f', 'C_smallStrike': 'F',
                  'P_bigStrike': 'r', 'P_smallStrike': 'R'},
            '7': {'exp': 'G', 'C_bigStrike': 'g', 'C_smallStrike': 'G',
                  'P_bigStrike': 's', 'P_smallStrike': 'S'},
            '8': {'exp': 'H', 'C_bigStrike': 'h', 'C_smallStrike': 'H',
                  'P_bigStrike': 't', 'P_smallStrike': 'T'},
            '9': {'exp': 'I', 'C_bigStrike': 'i', 'C_smallStrike': 'I',
                  'P_bigStrike': 'u', 'P_smallStrike': 'U'},
            '10': {'exp': 'J', 'C_bigStrike': 'j', 'C_smallStrike': 'J',
                   'P_bigStrike': 'v', 'P_smallStrike': 'V'},
            '11': {'exp': 'K', 'C_bigStrike': 'k', 'C_smallStrike': 'K',
                   'P_bigStrike': 'w', 'P_smallStrike': 'W'},
            '12': {'exp': 'L', 'C_bigStrike': 'l', 'C_smallStrike': 'L',
                   'P_bigStrike': 'x', 'P_smallStrike': 'X'}}

        ident_all = {
            '1':  {'exp': 'A', 'C': 'A', 'P': 'M'},
            '2':  {'exp': 'B', 'C': 'B', 'P': 'N'},
            '3':  {'exp': 'C', 'C': 'C', 'P': 'O'},
            '4':  {'exp': 'D', 'C': 'D', 'P': 'P'},
            '5':  {'exp': 'E', 'C': 'E', 'P': 'Q'},
            '6':  {'exp': 'F', 'C': 'F', 'P': 'R'},
            '7':  {'exp': 'G', 'C': 'G', 'P': 'S'},
            '8':  {'exp': 'H', 'C': 'H', 'P': 'T'},
            '9':  {'exp': 'I', 'C': 'I', 'P': 'U'},
            '10': {'exp': 'J', 'C': 'J', 'P': 'V'},
            '11': {'exp': 'K', 'C': 'K', 'P': 'W'},
            '12': {'exp': 'L', 'C': 'L', 'P': 'X'}}

        if exchange == 'OPQ':
            if self.strike > 999.999:
                exp_month_code = ident_opra[str(
                    self.maturity.month)][self.opt_type + '_bigStrike']
            else:
                exp_month_code = ident_opra[str(
                    self.maturity.month)][self.opt_type + '_smallStrike']
        else:
            exp_month_code = ident_all[str(self.maturity.month)][self.opt_type]

        if self.maturity < datetime.now():
            expired = '^' + \
                ident_all[str(self.maturity.month)]['exp'] + \
                str(self.maturity.year)[-2:]
        else:
            expired = ''

        return exp_month_code, expired

    def RIC_prices(self, ric, ricPrices):
        if self.debug:
            print(f"ricPrices's ric: {ric}")
            print(f"self.maturity: {self.maturity}")
        ric, prices, full_prices = self.Check_ric(ric, self.maturity)
        if prices is not None:
            valid_ric = {ric: prices, f'{ric} fullest prices': full_prices}
            ricPrices['valid_ric'].append(valid_ric)
        else:
            ricPrices['potential_rics'].append(ric)

        return ricPrices

    def Construct_RIC(self):
        asset_exchange = self.Get_asset_and_exchange()
        supported_exchanges = ['OPQ', 'IEU', 'EUX', 'HKG', 'HFE', 'OSA']
        ricPrices = {'valid_ric': [], 'potential_rics': []}
        for exchange, asset in asset_exchange.items():
            if exchange in supported_exchanges:
                strike_ric = self.Get_strike(exchange)
                exp_month_code, expired = self.Get_exp_month(exchange)

                if exchange == 'OPQ':
                    ric = asset + exp_month_code + \
                        str(self.maturity.day) + \
                        str(self.maturity.year)[-2:] + \
                        strike_ric + '.U' + expired
                    ricPrices = self.RIC_prices(ric, ricPrices)

                elif exchange == 'HKG' or exchange == 'HFE':
                    gen_len = ['0', '1', '2', '3']
                    if exchange == 'HFE':
                        gen_len = ['']
                    for i in gen_len:
                        exchs = {'HKG': {'exch_code': '.HK', 'gen': str(i)},
                                 'HFE': {'exch_code': '.HF', 'gen': ''}}
                        ric = asset + strike_ric + exchs[exchange]['gen'] + exp_month_code + str(
                            self.maturity.year)[-1:] + exchs[exchange]['exch_code'] + expired
                        ricPrices = self.RIC_prices(ric, ricPrices)

                elif exchange == 'OSA':
                    for jnet in ['', 'L', 'R']:
                        if self.asset[0] == '.':
                            ric = asset + jnet + strike_ric + exp_month_code + \
                                str(self.maturity.year)[-1:] + '.OS' + expired
                            ricPrices = self.RIC_prices(ric, ricPrices)
                        else:
                            for gen in ['Y', 'Z', 'A', 'B', 'C']:
                                ric = asset + jnet + gen + strike_ric + exp_month_code + \
                                    str(self.maturity.year)[-1:] + \
                                    '.OS' + expired
                                ricPrices = self.RIC_prices(ric, ricPrices)

                elif exchange == 'EUX' or exchange == 'IEU':
                    exchs = {'EUX': '.EX', 'IEU': '.L'}
                    for gen in ['', 'a', 'b', 'c', 'd']:
                        ric = asset + strike_ric + gen + exp_month_code + \
                            str(self.maturity.year)[-1:] + \
                            exchs[exchange] + expired
                        if self.debug: print(f"Construct_RIC's ric: {ric}")
                        try:
                            ricPrices = self.RIC_prices(ric, ricPrices)
                        except:
                            if self.debug:
                                print("Error for self.RIC_prices(ric, ricPrices)")
            else:
                print(f'The {exchange} exchange is not supported yet')
        return ricPrices


timeOfCalc3 = "2023-02-01"
indexUnderlying3 = ".HSI"


timeOfCalcDatetime2 = datetime.strptime(timeOfCalc3, '%Y-%m-%d')
currentUnderlyingPrc2 = rd.get_history(
    universe=[indexUnderlying3],
    start=timeOfCalc3,  # , end: "OptDateTime"=None
    fields=["TRDPRC_1"],
    interval="tick").iloc[-1][0]
currentUnderlyingPrc2

20192.4


int(round(currentUnderlyingPrc2, -2))

20200


HSI_test1 = Option_RIC(
    maturity=timeOfCalc3,
    strike=int(round(currentUnderlyingPrc2, -2)),
    opt_type='P',
    asset=indexUnderlying3,
    debug=False)
HSI_test2 = HSI_test1.Construct_RIC()


list(HSI_test2['valid_ric'][0].keys())[0]

'HSI20200N3.HF^B23'


HSI_test3 = HSI_test2['valid_ric'][0][list(HSI_test2['valid_ric'][0].keys())[0]].data.df.head()
HSI_test3


print(list(HSI_test2['valid_ric'][0].keys())[1])
HSI_test4 = HSI_test2['valid_ric'][0][list(HSI_test2['valid_ric'][0].keys())[1]]
HSI_test4.head()

HSI20200N3.HF^B23 fullest prices


timeOfCalc3 = '2022-02-10'
indexUnderlying3 = ".SPX"
timeOfCalcDatetime3 = datetime.strptime(timeOfCalc3, '%Y-%m-%d')
currentUnderlyingPrc3 = rd.get_history(
    universe=[indexUnderlying3],
    start=timeOfCalc3,  # , end: "OptDateTime"=None
    fields=["TRDPRC_1"],
    interval="tick").iloc[-1][0]
currentUnderlyingPrc3

3971.27


SPX_test2 = Option_RIC(
    maturity='2022-01-21',
    strike=int(round(currentUnderlyingPrc3, -2)),
    opt_type='P',
    asset=indexUnderlying3,
    debug=False)
SPX_test2 = SPX_test2.Construct_RIC()


list(SPX_test2['valid_ric'][0].keys())[0]

'SPXm212240000.U^A22'


SPX_test2['valid_ric'][0][list(SPX_test2['valid_ric'][0].keys())[0]].data.df


print(list(SPX_test2['valid_ric'][0].keys())[1])
SPX_test2['valid_ric'][0][list(SPX_test2['valid_ric'][0].keys())[1]]

SPXm212240000.U^A22 fullest prices


list(HSI_test2['valid_ric'][0].keys())[0]

'HSI20200N3.HF^B23'


HSICurr = rd.get_data(
    universe='.HSI',
    fields=["CF_CURR"])
HSICurr


# Now we will try and find the strike price for the option found.
# If we were to use the logic above, in the function `Option_RIC().Get_strike()`, we would find a list of all the possible prices for options on this underlying, which is too large of a group.
# We will use the name of the outputed option, which includes the strike:
import re  # native Python library that allows us to manipulate strings
hist_opt_found_strk_pr = re.findall(
    '(\d+|[A-Za-z]+)',  # This will split the string out of its numerical and non-numerical characters.
    list(HSI_test2['valid_ric'][0].keys())[0])[1][0:-1]  # `[1]` here skips through 'HSI' and to the numbers. `[0:-1]` here is there to ignore the last digit, which is not part of the strike price.
hist_opt_found_strk_pr

'2020'


hk_rf = 100 - rd.get_history(
    universe=['HK3MT=RR'],  # HK10YGB=EODF, HKGOV3MZ=R, HK3MT=RR
    fields=['TR.MIDPRICE'],
    start=HSI_test4.index[0].strftime('%Y-%m-%d'),
    end=HSI_test4.index[-1].strftime('%Y-%m-%d'))  # .iloc[::-1]  # `.iloc[::-1]` is here so that the resulting data-frame is the same order as `HSI_test5` so we can merge them later
hk_rf


HSI_test5 = pd.merge(
    HSI_test4, hk_rf,
    left_index=True, right_index=True)
HSI_test5 = HSI_test5.rename(
    columns={"SETTLE": "OptionPrice", "Mid Price": "RfRatePrct"})
HSI_test5.head()


hist_HSI_undrlying_pr = rd.get_history(
    universe=['.HSI'],
    fields=["TRDPRC_1"],
    # interval="1D",
    start=HSI_test4.index[0].strftime('%Y-%m-%d'),
    end=HSI_test4.index[-1].strftime('%Y-%m-%d'))  # .iloc[::-1]  # `.iloc[::-1]` is here so that the resulting data-frame is the same order as `HSI_test5` so we can merge them later
hist_HSI_undrlying_pr.head(2)


HSI_test6 = pd.merge(HSI_test5, hist_HSI_undrlying_pr,
                    left_index=True, right_index=True)
HSI_test6 = HSI_test6.rename(
    columns={"TRDPRC_1": "UndrlyingPr"})
HSI_test6.columns.name = list(HSI_test2['valid_ric'][0].keys())[0]  # This is to name the data-frame. Technically it names the column set, but the're all just for one instrument, so it's the same difference.
HSI_test6.head(2)


list(HSI_test2['valid_ric'][0].keys())[0]

'HSI20200N3.HF^B23'


HSI_test_start = HSI_test2['valid_ric'][0][list(HSI_test2['valid_ric'][0].keys())[1]].index[0]
# HSI_test_end = HSI_test2['valid_ric'][0][list(HSI_test2['valid_ric'][0].keys())[1]].index[-1]
(HSI_test1.maturity - HSI_test_start).days/365  # Expecting this to `YearsToExpiry` which is in 'DaysToExpiry / 365'.

0.2328767123287671


HSI_test2['valid_ric'][0][
    f"{list(HSI_test2['valid_ric'][0].keys())[0]} fullest prices"].head(2)


HSI_test2['valid_ric'][0][list(HSI_test2['valid_ric'][0].keys())[0]].data.df.head(2)


# rd.content.historical_pricing.summaries.Definition(
#     list(HSI_test2['valid_ric'][0].keys())[0],
#     start='2022-01-01',
#     end='2024-02-27',
#     interval=rd.content.historical_pricing.Intervals.DAILY,
#     fields=['TRDPRC_1', 'BID', 'ASK', 'EXPIR_DATE', 'TR.FOFirstTradingDate']).get_data().data.df


HSI_test2_exp_date = HSI_test1.maturity.strftime('%Y-%m-%d')
HSI_test2_exp_date

'2023-02-01'


# df = [
#     option.Definition(
#         underlying_type=option.UnderlyingType.ETI,
#         buy_sell='Buy',
#         instrument_code=list(HSI_test2['valid_ric'][0].keys())[0],  # 'STXE42000D3.EX' #  'HSI19300N3.HF^B23',  # list(HSI_test2['valid_ric'][0].keys())[0],
#         strike=float(hist_opt_found_strk_pr),
#         pricing_parameters=option.PricingParameters(
#             market_value_in_deal_ccy=float(HSI_test6['OptionPrice'][i]),
#             risk_free_rate_percent=float(HSI_test6['RfRatePrct'][i]),
#             underlying_price=float(HSI_test6['UndrlyingPr'][i]),
#             pricing_model_type='BlackScholes',
#             volatility_type='Implied',
#             underlying_time_stamp='Default',
#             report_ccy='HKD'
#         ))
#     for i in range(len(HSI_test6.index))]


option.Definition


hist_daily_universe_l = [
    option.Definition(
        instrument_tag='hist_daily_universe_l',
        end_date=HSI_test2_exp_date,
        buy_sell='Buy',
        call_put='Call',
        exercise_style='AMER',  # 'EURO'
        underlying_type=option.UnderlyingType.ETI,
        strike=float(hist_opt_found_strk_pr),
        tenor=str((HSI_test1.maturity - HSI_test_start).days/365),  # Expecting this to `YearsToExpiry` which is in 'DaysToExpiry / 365'.
        notional_ccy='HKD',
        # notional_amount=,
        # asian_definition=,
        # barrier_definition=,
        # binary_definition=,
        # double_barrier_definition=,
        # double_binary_definition=,
        # dual_currency_definition=,
        # forward_start_definition=,
        # underlying_definition=,
        delivery_date=HSI_test1.maturity.strftime('%Y-%m-%d'),
        instrument_code='STXE42000D3.EX',  # 'STXE42000D3.EX' #  'HSI19300N3.HF^B23',  # list(HSI_test2['valid_ric'][0].keys())[0],
        # cbbc_definition=,
        # double_barriers_definition=,
        # deal_contract=,
        # end_date_time=,
        # lot_size=,
        # offset=,
        # extended_params=,
        pricing_parameters=option.PricingParameters(
            valuation_date=HSI_test6.index[i].strftime('%Y-%m-%d'),
            market_value_in_deal_ccy=float(HSI_test6['OptionPrice'][i]),
            risk_free_rate_percent=float(HSI_test6['RfRatePrct'][i]),
            underlying_price=float(HSI_test6['UndrlyingPr'][i]),
            pricing_model_type='BlackScholes',
            volatility_type='Implied',
            underlying_time_stamp='Default',
            report_ccy='HKD'
        ))
    for i in range(len(HSI_test6.index))]


# hist_daily_universe_l = [
#     option.Definition(
#         underlying_type=option.UnderlyingType.ETI,
#         buy_sell='Buy',
#         instrument_code=list(HSI_test2['valid_ric'][0].keys())[0],  # 'STXE42000D3.EX' #  'HSI19300N3.HF^B23',  # list(HSI_test2['valid_ric'][0].keys())[0],
#         strike=float(hist_opt_found_strk_pr),
#         pricing_parameters=option.PricingParameters(
#             valuation_date=HSI_test6.index[i].strftime('%Y-%m-%d'),
#             market_value_in_deal_ccy=float(HSI_test6['OptionPrice'][i]),
#             risk_free_rate_percent=float(HSI_test6['RfRatePrct'][i]),
#             underlying_price=float(HSI_test6['UndrlyingPr'][i]),
#             pricing_model_type='BlackScholes',
#             volatility_type='Implied',
#             underlying_time_stamp='Default',
#             report_ccy='HKD'
#         ))
#     for i in range(len(HSI_test6.index))]


batchOf = 100
for i, j in enumerate(Chunks(hist_daily_universe_l, batchOf)):
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(hist_daily_universe_l, 100)])} started")
    # Example request with Body Parameter - Symbology Lookup
    response6 = rdf.Definitions(universe=j, fields=requestFields)
    response6 = response6.get_data()
    if i == 0:
        response6df = response6.data.df
    else:
        response6df = response6df.append(response6.data.df, ignore_index=True)
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(hist_daily_universe_l, 100)])} ended")

Batch of 61 requests no. 1/1 started

---------------------------------------------------------------------------
RDError                                   Traceback (most recent call last)
Input In [150], in <cell line: 2>()
      4 # Example request with Body Parameter - Symbology Lookup
      5 response6 = rdf.Definitions(universe=j, fields=requestFields)
----> 6 response6 = response6.get_data()
      7 if i == 0:
      8     response6df = response6.data.df

File c:\users\u6082174.ten\appdata\local\programs\python\python38-32\lib\site-packages\refinitiv\data\content\ipa\_ipa_content_provider.py:134, in IPAContentProviderLayer.get_data(self, session, on_response, async_mode)
    131     self._raise_error_no_async_url()
    133 else:
--> 134     response = super().get_data(session, on_response)
    136 return response

File c:\users\u6082174.ten\appdata\local\programs\python\python38-32\lib\site-packages\refinitiv\data\content\_content_provider.py:182, in ContentUsageLoggerMixin.get_data(self, session, on_response, **kwargs)
    173 def get_data(self, session=None, on_response=None, **kwargs) -> T:
    174     # Library usage logging
    175     get_usage_logger().log_func(
    176         name=f"{self.__class__.__module__}."
    177         f"{self.__class__.__qualname__}."
   (...)
    180         desc={FilterType.SYNC, FilterType.LAYER_CONTENT},
    181     )
--> 182     return super().get_data(session, on_response, **kwargs)

File c:\users\u6082174.ten\appdata\local\programs\python\python38-32\lib\site-packages\refinitiv\data\delivery\_data\_data_provider.py:769, in DataProviderLayer.get_data(self, session, on_response, **kwargs)
    765 response = self._provider.get_data(
    766     session, url, auto_retry=auto_retry, **kwargs, **self._kwargs
    767 )
    768 on_response and emit_event(on_response, response, self, session)
--> 769 self._check_response(response, config)
    770 return response

File c:\users\u6082174.ten\appdata\local\programs\python\python38-32\lib\site-packages\refinitiv\data\delivery\_data\_data_provider.py:748, in DataProviderLayer._check_response(self, response, config)
    747 def _check_response(self, response: BaseResponse, config):
--> 748     _check_response(response, config)

File c:\users\u6082174.ten\appdata\local\programs\python\python38-32\lib\site-packages\refinitiv\data\delivery\_data\_data_provider.py:636, in _check_response(response, config, response_class)
    633     error = RDError(error_code, error_message)
    635 error.response = response
--> 636 raise error

RDError: Error code 400 | Invalid input: Unbindable json. Could not find member 'tenor' on object of type 'EtiOptionDefinition'. Path 'tenor', line 1, position 112.


response6


HSIdf = response6df.copy()
HSIdf.rename(columns={"Volatility": 'ImpliedVolatility'}, inplace=True)
HSIdf


HSIdf[5:40]


HSI_test6_del_lay_list = [
        {
          "instrumentType": "Option",
          "instrumentDefinition": {
            "buySell": "Buy",
            "underlyingType": "Eti",
            "instrumentCode": list(HSI_test2['valid_ric'][0].keys())[0],  # "instrumentCode": None,
            "strike": float(hist_opt_found_strk_pr),
          },
          "pricingParameters": {
            "marketValueInDealCcy": float(HSI_test6['OptionPrice'][i]),
            "riskFreeRatePercent": float(HSI_test6['RfRatePrct'][i]),
            "underlyingPrice": float(HSI_test6['UndrlyingPr'][i]),
            "pricingModelType": "BlackScholes",
            "dividendType": "ImpliedYield",
            "volatilityType": "Implied",
            "underlyingTimeStamp": "Default",
            "reportCcy": "HKD"
          }
        }
      for i in range(len(HSI_test6.index))]


batchOf = 100
for i, j in enumerate(Chunks(HSI_test6_del_lay_list, batchOf)):
    print(f"Batch of {batchOf} requests no. {str(i+1)}/{str(len([i for i in Chunks(HSI_test6_del_lay_list, batchOf)]))} started")
    # Example request with Body Parameter - Symbology Lookup
    request_definition = rd.delivery.endpoint_request.Definition(
        method=rd.delivery.endpoint_request.RequestMethod.POST,
        url='https://api.refinitiv.com/data/quantitative-analytics/v1/financial-contracts',
        body_parameters={"fields": requestFields,
                         "outputs": ["Data", "Headers"],
                         "universe": j})

    response8 = request_definition.get_data()
    headers_name = [h['name'] for h in response8.data.raw['headers']]

    if i == 0:
        response8df = pd.DataFrame(
            data=response8.data.raw['data'], columns=headers_name)
        print({"fields": requestFields,
           "outputs": ["Data", "Headers"],
           "universe": j})
    else:
        _response8df = pd.DataFrame(
            data=response8.data.raw['data'], columns=headers_name)
        response8df = response8df.append(_response8df, ignore_index=True)
    # display(_response8df)
    print(f"Batch of {batchOf} requests no. {str(i+1)}/{str(len([i for i in Chunks(HSI_test6_del_lay_list, batchOf)]))} ended")

Batch of 100 requests no. 1/1 started
{'fields': ['MarketValueInDealCcy', 'RiskFreeRatePercent', 'UnderlyingPrice', 'PricingModelType', 'DividendType', 'UnderlyingTimeStamp', 'ReportCcy', 'VolatilityType', 'Volatility', 'DeltaPercent', 'GammaPercent', 'RhoPercent', 'ThetaPercent', 'VegaPercent'], 'outputs': ['Data', 'Headers'], 'universe': [{'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 3352.0, 'riskFreeRatePercent': 0.7365000000000066, 'underlyingPrice': 16358.52, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 3656.0, 'riskFreeRatePercent': 0.7309999999999945, 'underlyingPrice': 16081.04, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 2615.0, 'riskFreeRatePercent': 0.7129999999999939, 'underlyingPrice': 17325.66, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 2355.0, 'riskFreeRatePercent': 0.7099999999999937, 'underlyingPrice': 17619.71, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1931.0, 'riskFreeRatePercent': 0.7964999999999947, 'underlyingPrice': 18343.12, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1984.0, 'riskFreeRatePercent': 0.7944999999999993, 'underlyingPrice': 18256.48, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 2084.0, 'riskFreeRatePercent': 0.7860000000000014, 'underlyingPrice': 18045.66, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 2092.0, 'riskFreeRatePercent': 0.7780000000000058, 'underlyingPrice': 17992.54, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 2309.0, 'riskFreeRatePercent': 0.7920000000000016, 'underlyingPrice': 17655.91, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 2441.0, 'riskFreeRatePercent': 0.9705000000000013, 'underlyingPrice': 17424.41, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 2339.0, 'riskFreeRatePercent': 0.9814999999999969, 'underlyingPrice': 17523.81, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 2237.0, 'riskFreeRatePercent': 0.9779999999999944, 'underlyingPrice': 17660.9, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 2299.0, 'riskFreeRatePercent': 0.9380000000000024, 'underlyingPrice': 17573.58, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 2518.0, 'riskFreeRatePercent': 0.9779999999999944, 'underlyingPrice': 17297.94, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1854.0, 'riskFreeRatePercent': 1.1654999999999944, 'underlyingPrice': 18204.68, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1666.0, 'riskFreeRatePercent': 1.1700000000000017, 'underlyingPrice': 18597.23, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1551.0, 'riskFreeRatePercent': 1.159000000000006, 'underlyingPrice': 18736.44, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1547.0, 'riskFreeRatePercent': 1.117999999999995, 'underlyingPrice': 18675.35, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1131.0, 'riskFreeRatePercent': 1.1460000000000008, 'underlyingPrice': 19518.29, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1108.0, 'riskFreeRatePercent': 1.2554999999999978, 'underlyingPrice': 19441.18, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1405.0, 'riskFreeRatePercent': 1.0949999999999989, 'underlyingPrice': 18814.82, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1085.0, 'riskFreeRatePercent': 1.073999999999998, 'underlyingPrice': 19450.23, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 897.0, 'riskFreeRatePercent': 1.0420000000000016, 'underlyingPrice': 19900.87, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1124.0, 'riskFreeRatePercent': 1.0829999999999984, 'underlyingPrice': 19463.63, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1014.0, 'riskFreeRatePercent': 1.1585000000000036, 'underlyingPrice': 19596.2, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 932.0, 'riskFreeRatePercent': 1.1099999999999994, 'underlyingPrice': 19673.45, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1061.0, 'riskFreeRatePercent': 1.078000000000003, 'underlyingPrice': 19368.59, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1056.0, 'riskFreeRatePercent': 0.9909999999999997, 'underlyingPrice': 19450.67, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1018.0, 'riskFreeRatePercent': 0.9909999999999997, 'underlyingPrice': 19352.81, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1209.0, 'riskFreeRatePercent': 0.9334999999999951, 'underlyingPrice': 19094.8, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 1149.0, 'riskFreeRatePercent': 0.7944999999999993, 'underlyingPrice': 19160.49, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 864.0, 'riskFreeRatePercent': 0.8100000000000023, 'underlyingPrice': 19679.22, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 934.0, 'riskFreeRatePercent': 0.6465000000000032, 'underlyingPrice': 19593.06, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 934.0, 'riskFreeRatePercent': 0.6465000000000032, 'underlyingPrice': 19593.06, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 934.0, 'riskFreeRatePercent': 0.6465000000000032, 'underlyingPrice': 19593.06, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 714.0, 'riskFreeRatePercent': 0.6564999999999941, 'underlyingPrice': 19898.91, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 836.0, 'riskFreeRatePercent': 0.6659999999999968, 'underlyingPrice': 19741.14, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 807.0, 'riskFreeRatePercent': 0.6779999999999973, 'underlyingPrice': 19781.41, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 807.0, 'riskFreeRatePercent': 0.6779999999999973, 'underlyingPrice': 19781.41, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 674.0, 'riskFreeRatePercent': 0.7604999999999933, 'underlyingPrice': 20145.29, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 461.0, 'riskFreeRatePercent': 0.7254999999999967, 'underlyingPrice': 20793.11, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 382.0, 'riskFreeRatePercent': 0.751499999999993, 'underlyingPrice': 21052.17, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 389.0, 'riskFreeRatePercent': 0.742999999999995, 'underlyingPrice': 20991.64, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 281.0, 'riskFreeRatePercent': 0.7390000000000043, 'underlyingPrice': 21388.34, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 253.0, 'riskFreeRatePercent': 0.7745000000000033, 'underlyingPrice': 21331.46, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 203.0, 'riskFreeRatePercent': 0.7535000000000025, 'underlyingPrice': 21436.05, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 191.0, 'riskFreeRatePercent': 0.742999999999995, 'underlyingPrice': 21514.1, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 130.0, 'riskFreeRatePercent': 0.7180000000000035, 'underlyingPrice': 21738.66, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 127.0, 'riskFreeRatePercent': 0.7169999999999987, 'underlyingPrice': 21746.72, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 127.0, 'riskFreeRatePercent': 0.7295000000000016, 'underlyingPrice': 21577.64, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 103.0, 'riskFreeRatePercent': 0.707499999999996, 'underlyingPrice': 21678.0, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 106.0, 'riskFreeRatePercent': 0.6640000000000015, 'underlyingPrice': 21650.98, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 74.0, 'riskFreeRatePercent': 0.5955000000000013, 'underlyingPrice': 22044.65, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 74.0, 'riskFreeRatePercent': 0.5955000000000013, 'underlyingPrice': 22044.65, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 74.0, 'riskFreeRatePercent': 0.5955000000000013, 'underlyingPrice': 22044.65, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 74.0, 'riskFreeRatePercent': 0.5955000000000013, 'underlyingPrice': 22044.65, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 40.0, 'riskFreeRatePercent': 0.5769999999999982, 'underlyingPrice': 22566.78, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 28.0, 'riskFreeRatePercent': 0.5319999999999965, 'underlyingPrice': 22688.9, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 60.0, 'riskFreeRatePercent': 0.5229999999999961, 'underlyingPrice': 22069.73, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 66.0, 'riskFreeRatePercent': 0.5745000000000005, 'underlyingPrice': 21842.33, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}, {'instrumentType': 'Option', 'instrumentDefinition': {'buySell': 'Buy', 'underlyingType': 'Eti', 'instrumentCode': 'HSI19800N3.HF^B23', 'strike': 1980.0}, 'pricingParameters': {'marketValueInDealCcy': 47.0, 'riskFreeRatePercent': 0.5925000000000011, 'underlyingPrice': 22072.18, 'pricingModelType': 'BlackScholes', 'dividendType': 'ImpliedYield', 'volatilityType': 'Implied', 'underlyingTimeStamp': 'Default', 'reportCcy': 'HKD'}}]}
Batch of 100 requests no. 1/1 ended


response8df


import nb_mypy  # !pip3 install nb_mypy --trusted-host pypi.org # https://pypi.org/project/nb-mypy/ # https://gitlab.tue.nl/jupyter-projects/nb_mypy/-/blob/master/Nb_Mypy.ipynb


%load_ext nb_mypy

Version 1.0.4


%reload_ext nb_mypy

Version 1.0.4


%nb_mypy On


%nb_mypy DebugOff


# %nb_mypy unknown


from datetime import date as dtdate
import pandas_market_calendars as mcal  # See `https://github.com/rsheftel/pandas_market_calendars/blob/master/examples/usage.ipynb` for info on this market calendar library
from typing import Tuple, Union, Dict, List, Any
import numpy as np
import calendar
from __future__ import annotations  # This native library allows us to use not-yet-fully-deffined classes as a Type Hint when inside that class.


import refinitiv.data as rd  # This is LSEG's Data and Analytics' API wrapper, called the Refinitiv Data Library for Python.
from refinitiv.data.content import historical_pricing  # We will use this Python Class in `rd` to show the Implied Volatility data already available before our work.
from refinitiv.data.content import search  # We will use this Python Class in `rd` to fid the instrument we are after, closest to At The Money.
from refinitiv.data.content.ipa.financial_contracts import option  # We're going to need thtis to use the content layer of the RD library and the calculators of greeks and Impl Volat in IPA & ETI
import refinitiv.data.content.ipa.financial_contracts as rdf  # We're going to need thtis to use the content layer of the RD library and the calculators of greeks and Impl Volat in Instrument Pricing Analytics (IPA) and Exchange Traded Instruments (ETI)

import numpy as np  # We need `numpy` for mathematical and array manipilations.
import pandas as pd  # We need `pandas` for datafame and array manipilations.
import calendar  # We use `calendar` to identify holidays and maturity dates of intruments of interest.
import pytz  # We use `pytz` to manipulate time values aiding `calendar` library. to import its types, you might need to run `!python3 -m pip install types-pytz`
import pandas_market_calendars as mcal  # Used to identify holidays. See `https://github.com/rsheftel/pandas_market_calendars/blob/master/examples/usage.ipynb` for info on this market calendar library
from datetime import datetime, timedelta, timezone  # We use these to manipulate time values
from dateutil.relativedelta import relativedelta  # We use `relativedelta` to manipulate time values aiding `calendar` library.

# `plotly` is a library used to render interactive graphs:
import plotly
import plotly.graph_objects as go
import plotly.express as px  # This is just to see the implied vol graph when that field is available
import matplotlib.pyplot as plt  # We use `matplotlib` to just in case users do not have an environment suited to `plotly`.
from IPython.display import display, clear_output  # We use `clear_output` for users who wish to loop graph production on a regular basis.

# Let's authenticate ourseves to LSEG's Data and Analytics service, Refinitiv:
try:  # The following libraries are not available in Codebook, thus this try loop
    rd.open_session(config_name="C:\\Example.DataLibrary.Python-main\\Example.DataLibrary.Python-main\\Configuration\\refinitiv-data.config.json")
    rd.open_session("desktop.workspace")
except:
    rd.open_session()
print(f"Here we are using the refinitiv Data Library version {rd.__version__}")

Here we are using the refinitiv Data Library version 1.0.0b24


class index_imp_vola_and_greeks_IPA_calc():  # All about Type Hints here: https://realpython.com/python-type-checking/#static-type-checking

    def __init__(  # Constroctor
        self,
        index_underlying: str = ".STOXX50E"
    ):

        self.index_underlying: str = index_underlying
        # self.expiryYearOfInterest: int = datetime.now().year
        # self.graphStyle: str = 'without out of trading hours'  # 'overlay', '3 graphs', 'simple'
        # self.graphTemplate: str = 'plotly_dark'
        # self.debug: bool = False
        # self.returnDfGraph: bool = False
        # # def change_attrs(self, **kwargs):  for kwarg in kwargs:    self.__setattr__(kwarg, kwargs[kwarg])

    def get_exp_dates(
        self,
        year: int = datetime.now().year,
        days: bool = True,
        mcal_get_calendar: str = 'EUREX'
    ) -> Dict[int, Union[dtdate, str]]:
        '''
        get_exp_dates Version 4.0:

        This function gets expiration dates for a year for NDX options, which are the 3rd Fridays of each month.

        Changes
        ----------------------------------------------
        Changed from Version 1.0 to 2.0: Jonathan Legrand changed Haykaz Aramyan's original code to allow
            (i) for the function's holiday argument to be changed, and defaulted to 'EUREX' as opposed to 'CBOE_Index_Options' and
            (ii) for the function to output full date objects as opposed to just days of the month if agument days=True.

        Changed from Version 2.0 to 3.0: Jonathan Legrand changed this function to reflec the fact that it can be used for indexes other than EUREX.

        Changed from Version 3.0 to 4.0: Jonathan Legrand changed this function to be in line with PEP 3107 (type hints).

        Dependencies
        ----------------------------------------------
        Python library 'pandas_market_calendars' version 3.2

        Parameters
        -----------------------------------------------
        Input:
            year(int): year for which expiration days are requested

            mcal_get_calendar(str): String of the calendar for which holidays have to be taken into account. More on this calendar (link to Github checked 2022-10-11): https://github.com/rsheftel/pandas_market_calendars/blob/177e7922c7df5ad249b0d066b5c9e730a3ee8596/pandas_market_calendars/exchange_calendar_cboe.py
                Default: mcal_get_calendar='EUREX'

            days(bool): If True, only days of the month is outputed, else it's dataeime objects
                Default: days=True

        Output
        -----------------------------------------------
            dates(dict): dictionary of expiration days for each month of a specified year in datetime.date format.
        '''

        i: int  # this is for the 'for loop' in this function coming below

        # get CBOE market holidays
        Cal: mcal.get_calendar = mcal.get_calendar(mcal_get_calendar)
        holidays: Tuple[np.datetime64, ...] = Cal.holidays().holidays

        # set calendar starting from Saturday
        c: calendar.Calendar = calendar.Calendar(firstweekday=calendar.SATURDAY)

        # get the 3rd Friday of each month
        exp_dates: dict = {}  # https://stackoverflow.com/questions/48054521/indicating-multiple-value-in-a-dict-for-type-hints
        date: dtdate
        for i in range(1, 13):
            date = c.monthdatescalendar(year, i)[2][-1]
            # check if found date is an holiday and get the previous date if it is
            if date in holidays:
                date = date + timedelta(-1)
            # append the date to the dictionary
            if year in exp_dates and days:
                exp_dates[year].append(date.day)
            elif year in exp_dates:
                exp_dates[year].append(date)
            elif days:
                exp_dates[year] = [date.day]
            else:
                exp_dates[year] = [date]

        return exp_dates

    def search_index_opt_ATM(
        self,
        debug: bool = False,
        after: int = 15,
        call_or_put: str = 'Put',
        searchFields: List[str] = ["ExchangeCode", "UnderlyingQuoteName"],
        include_weekly_opts: bool = False,
        topNuSearchResults: int = 10_000,
        timeOfCalcDatetime: datetime = datetime.now(),  # Here we allow for historical analysis.
        underMrktPriceField: str = "TRDPRC_1"
    ) -> index_imp_vola_and_greeks_IPA_calc:

        self.after = after
        self.timeOfCalcDatetime = timeOfCalcDatetime
        self.underMrktPriceField = underMrktPriceField
        i: int; j: dtdate; k: str  # this is for the 'for loop' in this function coming below

        self.exchangeC: str; self.exchangeRIC: str; self.mcalGetCalendar: str
        if self.index_underlying == ".STOXX50E":
            self.exchangeC, self.exchangeRIC, self.mcalGetCalendar = 'EUX', 'STX', 'EUREX'
        elif self.index_underlying == '.SPX':
            self.exchangeC, self.exchangeRIC, self.mcalGetCalendar = 'OPQ', 'SPX', 'CBOE_Futures'  # 'CBOE_Index_Options'  # should be 'CBOE_Index_Options'... CBOT_Equity

        timeOfCalcStr: str=timeOfCalcDatetime.strftime('%Y-%m-%d')
        fullDatesAtTimeOfCalc: dict = self.get_exp_dates(
            year=timeOfCalcDatetime.year,
            days=False,
            mcal_get_calendar=self.mcalGetCalendar)
        fullDatesAtTimeOfCalcDatetime: List[datetime] = [
            datetime(j.year, j.month, j.day)
            for j in fullDatesAtTimeOfCalc[
                list(fullDatesAtTimeOfCalc.keys())[0]]]
        expiryDateOfInt: datetime = [
            j for j in fullDatesAtTimeOfCalcDatetime
            if j > timeOfCalcDatetime + relativedelta(days=self.after)][0]

        if debug: print(f"expiryDateOfInt: {expiryDateOfInt}")

        # Certain search fields are nessesary for the next steps, so let's add them to the `searchFields` object:
        for k in ['DocumentTitle', 'RIC', 'StrikePrice', 'UnderlyingQuoteRIC'][::-1]:  # the `[::-1]` reverses the list
            searchFields.insert(0, k)

        # Now let's build our Search filter:
        _filter: str = f"RCSAssetCategoryLeaf eq 'Option' \
                        and RIC eq '{self.exchangeRIC}*' \
                        and CallPutOption eq '{call_or_put}' \
                        and ExchangeCode eq '{self.exchangeC}' \
                        and ExpiryDate ge {(expiryDateOfInt - relativedelta(days=1)).strftime('%Y-%m-%d')} \
                        and ExpiryDate lt {(expiryDateOfInt + relativedelta(days=1)).strftime('%Y-%m-%d')}"
        if not include_weekly_opts:
            _filter += " and DocumentTitle ne '*Weekly*'"

        response1 = search.Definition(
            view=search.Views.SEARCH_ALL,  # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`
            query=self.index_underlying,
            select=', '.join(map(str, searchFields)),
            filter=_filter,  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.
            top=topNuSearchResults,
        ).get_data()
        self.searchDf: pd.DataFrame = response1.data.df
        searchDf: pd.DataFrame = self.searchDf

        if debug:
            print("searchDf")
            display(searchDf)

        try:
            self.underlyingPrice: str = rd.get_history(
                universe=[self.index_underlying],
                fields=[underMrktPriceField],
                interval="tick").iloc[-1][0]
        except:
            print("Function failed at the search strage, returning the following dataframe: ")
            display(searchDf)

        if debug:
            print(f"Underlying {self.index_underlying}'s price recorded here was {self.underlyingPrice}")
            display(searchDf.iloc[(searchDf.StrikePrice-self.underlyingPrice).abs().argsort()[:10]])

        self.instrument: str = searchDf.iloc[(
            searchDf.StrikePrice-self.underlyingPrice).abs().argsort()[:1]].RIC.values[0]
        self.instrumentInfo: pd.DataFrame = searchDf.iloc[(
            searchDf.StrikePrice-self.underlyingPrice).abs().argsort()[:1]]
        self.ATMOpt = self.instrument

        return self

    def IPA_calc(
        self,
        dateBack: int = 3,
        optnMrktPriceField: str = "TRDPRC_1",
        debug: bool = False,
        atOptionTradeOnly: bool = True,
        riskFreeRatePrct: Union[str, None] = None,
        riskFreeRatePrctField: Union[str, None] = None,
        timeZoneInGraph: datetime = datetime.now().astimezone(),
        requestFields: List[str] = [
            "DeltaPercent", "GammaPercent", "RhoPercent",
            "ThetaPercent", "VegaPercent"],
        searchBatchMax: int = 100
    ) -> index_imp_vola_and_greeks_IPA_calc:

        i: int  # Type Hinted for loops coming up below.
        k: str  # Type Hinted for loops coming up below.
        n: int  # Type Hinted for loops coming up below.
        m: int  # Type Hinted for loops coming up below.
        p: rdf._base_definition.BaseDefinition  # Type Hinted for loops coming up below.
        self.dateBack: int = dateBack
        self.start: dtdate = self.timeOfCalcDatetime - pd.tseries.offsets.BDay(
            self.dateBack)
        self.startStr: str = (self.timeOfCalcDatetime - pd.tseries.offsets.BDay(
            self.dateBack)).strftime('%Y-%m-%dT%H:%M:%S.%f')  # e.g.: '2022-10-05T07:30:00.000'
        self.endStr: str = self.timeOfCalcDatetime.strftime('%Y-%m-%dT%H:%M:%S.%f')

        _optnMrktPrice: pd.DataFrame = rd.get_history(
            universe=[self.instrument],
            fields=[optnMrktPriceField],
            interval="10min",
            start=self.startStr,  # Ought to always start at 4 am for OPRA exchanged Options, more info in the article below
            end=self.endStr)  # Ought to always end at 8 pm for OPRA exchanged Options, more info in the article below

        if _optnMrktPrice.empty:
            print(f"No data could be found for {self.instrument}, please check it on Refinitiv Workspace")

        if debug:
            print(self.instrument)
            display(_optnMrktPrice)

        # get a datapoint every 10 min
        optnMrktPrice: pd.DataFrame = _optnMrktPrice.resample(
            '10Min').mean()
        # Only keep trading days
        self.optnMrktPrice: pd.DataFrame = optnMrktPrice[
            optnMrktPrice.index.strftime('%Y-%m-%d').isin(
                [k for k in _optnMrktPrice.index.strftime('%Y-%m-%d').unique()])]
        # Forward Fill to populate NaN values
        self.optnMrktPrice.fillna(method='ffill', inplace=True)

        # Note also that one may want to only look at 'At Option Trade' datapoints,
        # i.e.: Implied Volatility when a trade is made for the Option, but not when
        # none is made. For this, we will use the 'At Trade' (`AT`) dataframes:
        if atOptionTradeOnly:
            self.AToptnMrktPrice: pd.DataFrame = _optnMrktPrice

        self.underlying: str = self.searchDf.iloc[
            (self.searchDf.StrikePrice).abs().argsort()[
                :1]].UnderlyingQuoteRIC.values[0][0]

        _underlyingMrktPrice: pd.DataFrame = rd.get_history(
            universe=[self.underlying],
            fields=[self.underMrktPriceField],
            interval="10min",
            start=self.startStr,
            end=self.endStr)

        # Let's put it al in one data-frame, `df`. Some datasets will have data
        # going from the time we set for `startStr` all the way to `endStr`. Some won't
        # because no trade happened in the past few minutes/hours. We ought to base
        # ourselves on the dataset with values getting closer to `end` and `ffill`
        # for the other column. As a result, the following `if` loop is needed:
        if optnMrktPrice.index[-1] >= _underlyingMrktPrice.index[-1]:
            df: pd.DataFrame = self.optnMrktPrice.copy()
            df[f"underlying {self.underlying} {self.underMrktPriceField}"] = _underlyingMrktPrice
        else:
            df = _underlyingMrktPrice.copy()
            df.rename(
                columns={self.underMrktPriceField:
                         f"underlying {self.underlying} {self.underMrktPriceField}"},
                inplace=True)
            df[self.underMrktPriceField] = self.optnMrktPrice
            df.columns.name = self.optnMrktPrice.columns.name
        df.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values
        selfdf: pd.DataFrame = df.dropna()

        if atOptionTradeOnly:
            ATunderlyingMrktPrice: pd.DataFrame = self.AToptnMrktPrice.join(
                _underlyingMrktPrice,
                rsuffix=f"_{self.underlying}_underlying",
                lsuffix=f"_{self.instrument}_OptPr",
                how='inner')

        self.strikePrice: pd.DataFrame = self.searchDf.iloc[
            (self.searchDf['StrikePrice']-self.underlyingPrice).abs().argsort()[
                :1]].StrikePrice.values[0]

        # I didn't think that I needed to Type Hint for the event when
        # `_riskFreeRatePrct` & `_riskFreeRatePrctField` were `None`, but Error Messages
        # suggest otherwise...
        _riskFreeRatePrct: Union[str, None]
        _riskFreeRatePrctField: Union[str, None]
        if riskFreeRatePrct is None and self.index_underlying == ".SPX":
            _riskFreeRatePrct, _riskFreeRatePrctField = 'USDCFCFCTSA3M=', 'TR.FIXINGVALUE'
        elif riskFreeRatePrct is None and self.index_underlying == ".STOXX50E":
            _riskFreeRatePrct, _riskFreeRatePrctField = 'EURIBOR3MD=', 'TR.FIXINGVALUE'
        elif riskFreeRatePrct is not None:
            _riskFreeRatePrct, _riskFreeRatePrctField = riskFreePrctRate, riskFreeRatePrctField
        self.riskFreeRatePrct: Union[str, None] = riskFreeRatePrct
        self.riskFreeRatePrctField: Union[str, None] = riskFreeRatePrctField

        _RfRatePrct: pd.DataFrame = rd.get_history(
            universe=[_riskFreeRatePrct],  # USD3MFSR=, USDSOFR=
            fields=[_riskFreeRatePrctField],
            # Since we will use `dropna()` as a way to select the rows we are after later on in the code, we need to ask for more risk-free data than needed, just in case we don't have enough:
            start=(self.start - timedelta(days=1)).strftime('%Y-%m-%d'),  # https://teamtreehouse.com/community/local-variable-datetime-referenced-before-assignment
            end=(self.timeOfCalcDatetime +
                 timedelta(days=1)).strftime('%Y-%m-%d'))

        self.RfRatePrct: pd.DataFrame = _RfRatePrct.resample(
            '10Min').mean().fillna(method='ffill')
        df['RfRatePrct'] = self.RfRatePrct
        self.df: pd.DataFrame = df.fillna(method='ffill')

        if atOptionTradeOnly:
            pd.options.mode.chained_assignment = None  # default='warn'
            ATunderlyingMrktPrice['RfRatePrct'] = [
                pd.NA for i in ATunderlyingMrktPrice.index]
            for i in self.RfRatePrct.index:
                _i: str = str(i)[:10]
                for n, m in enumerate(ATunderlyingMrktPrice.index):
                    if _i in str(m):
                        if len(self.RfRatePrct.loc[i].values) == 2:
                            ATunderlyingMrktPrice[
                                'RfRatePrct'].iloc[n] = self.RfRatePrct.loc[i].values[0][0]
                        elif len(self.RfRatePrct.loc[i].values) == 1:
                            ATunderlyingMrktPrice[
                                'RfRatePrct'].iloc[n] = self.RfRatePrct.loc[i].values[0]
            self.ATdf: pd.DataFrame = ATunderlyingMrktPrice.copy().fillna(method='ffill')  # This is in case there were no Risk Free datapoints released after a certain time, but trades on the option still went through.

        if timeZoneInGraph != 'GMT':
            if atOptionTradeOnly:
                self.ATdf.index = [
                    self.ATdf.index[i].replace(
                        tzinfo=pytz.timezone(
                            'GMT')).astimezone(
                        tz=datetime.now().astimezone().tzinfo)
                    for i in range(len(self.ATdf))]
            else:
                df.index = [
                    df.index[i].replace(
                        tzinfo=pytz.timezone(
                            'GMT')).astimezone(
                        tz=timeZoneInGraph.tzinfo)
                    for i in range(len(df))]

        # Define our message to the calculation endpoint in the RDP (Refinitiv Data Platform) API, `atOptionTradeOnly`:
        self.universeL: List[rdf._base_definition.BaseDefinition]
        if atOptionTradeOnly:
            self.universeL = [
                option.Definition(
                    underlying_type=option.UnderlyingType.ETI,
                    buy_sell='Buy',
                    instrument_code=self.instrument,
                    strike=float(self.strikePrice),
                    pricing_parameters=option.PricingParameters(
                        market_value_in_deal_ccy=float(
                            self.ATdf[
                                f"{optnMrktPriceField}_{self.instrument}_OptPr"][i]),
                        risk_free_rate_percent=float(self.ATdf['RfRatePrct'][i]),
                        underlying_price=float(
                            self.ATdf[
                                f"{self.underMrktPriceField}_{self.underlying}_underlying"][i]),
                        pricing_model_type='BlackScholes',
                        volatility_type='Implied',
                        underlying_time_stamp='Default',
                        report_ccy='EUR'))
                for i in range(len(self.ATdf.index))]
        else:
            self.universeL = [
                option.Definition(
                    underlying_type=option.UnderlyingType.ETI,
                    buy_sell='Buy',
                    instrument_code=self.instrument,
                    strike=float(self.strikePrice),
                    pricing_parameters=option.PricingParameters(
                        market_value_in_deal_ccy=float(df[optnMrktPriceField][i]),
                        risk_free_rate_percent=float(df.RfRatePrct[i]),
                        underlying_price=float(
                            df[f"underlying {self.underlying} {self.underMrktPriceField}"][i]),
                        pricing_model_type='BlackScholes',
                        volatility_type='Implied',
                        underlying_time_stamp='Default',
                        report_ccy='EUR'))
                for i in range(len(df.index))]

        # We would like to keep a minimum of these fields in the Search Responce in order to construct following graphs:
        for k in ["MarketValueInDealCcy", "RiskFreeRatePercent",
                  "UnderlyingPrice", "Volatility"][::-1]:
            requestFields.insert(0, k)
        self.requestFields: List[str] = requestFields

        for i, p in enumerate(
            [self.universeL[i:i+searchBatchMax]
             for i in range(0, len(self.universeL), searchBatchMax)]):  # This list chunks our `universeL` in batches of `searchBatchMax`

            _IPADf: pd.DataFrame = rdf.Definitions(
                universe=p, fields=requestFields).get_data().data.df
            if i == 0:
                self.IPADf: pd.DataFrame = _IPADf
            else:
                self.IPADf: pd.DataFrame = self.IPADf.append(
                    _IPADf, ignore_index=True)

        if atOptionTradeOnly:
            self.IPADf.index = self.ATdf.index
        else:
            self.IPADf.index = self.df.index

        self.atOptionTradeOnly: bool = atOptionTradeOnly

        return self

    def Simple_graph(
        self,
        maxColwidth: int = 200,
        size: Tuple[int, int] = (15, 5),
        lineStyle: str = '.-',  # 'o-'
        plotting: str = 'Volatility',
        displayIndexInfo: bool = False
    ) -> index_imp_vola_and_greeks_IPA_calc:

        # We are going to want to show details about data retreived in a dataframe in the output of this function. The one line below allows us to maximise the width (column) length of cells to see all that is written within them.
        if displayIndexInfo:
            pd.options.display.max_colwidth = maxColwidth
            display(self.instrumentInfo)
        IPADfSimpleGraph: pd.DataFrame = pd.DataFrame(
            data=self.IPADf[[plotting]].values,
            index=self.IPADf[[plotting]].index)
        fig, axes = plt.subplots(ncols=1, figsize=size)
        axes.plot(IPADfSimpleGraph, lineStyle)
        if self.atOptionTradeOnly:
            axes.set_title(f"{self.instrument} {plotting} At Trade Only")
        else:
            axes.set_title(f"{self.instrument} {plotting}")

        self.plt = plt

        return self

    def Graph(
        self,
        include: Union[None, List[str]] = None,
        graphTemplate: str = 'plotly_dark',
        debug: bool=False
    ) -> index_imp_vola_and_greeks_IPA_calc:

        if include is None:
            include = self.requestFields

        self.IPADfGraph = self.IPADf[include]
        if debug: display(self.IPADfGraph)
        self.fig = px.line(self.IPADfGraph)

        # # Seems like the below (comented out) is resolved. Leaving it for future debugging if needed.
        # try:  # This is needed in case there is not enough data to calculate values for all timestamps , see https://stackoverflow.com/questions/67244912/wide-format-csv-with-plotly-express
        #     self.IPADfGraph = self.IPADf[include]
        #     if debug: display(self.IPADfGraph)
        #     self.fig = px.line(self.IPADfGraph)
        # except:
        #     try:
        #         print(f"Not all fields could be graphed: {include}")
        #         self.IPADfGraph = self.IPADfGraph[
        #             ["Volatility", "MarketValueInDealCcy",
        #              "RiskFreeRatePercent", "UnderlyingPrice"]]
        #         self.fig = px.line(self.IPADfGraph)
        #     except:
        #         print(f"Not all fields could be graphed: ['Volatility', 'MarketValueInDealCcy', 'RiskFreeRatePercent', 'UnderlyingPrice']")
        #         self.IPADfGraph = self.IPADfGraph[
        #             ["Volatility", "MarketValueInDealCcy",
        #              "RiskFreeRatePercent", "UnderlyingPrice"]]
        #         self.fig = px.line(self.IPADfGraph)

        self.graphTemplate = graphTemplate

        return self

    def Overlay(
        self
    ) -> index_imp_vola_and_greeks_IPA_calc:

        self.fig.update_layout(
            title=self.instrument,
            template=self.graphTemplate)
        self.fig.for_each_trace(
            lambda t: t.update(
                visible=True if t.name in self.IPADfGraph.columns[:1] else "legendonly"))

        return self

    def Stack3(
        self,
        autosize: bool = False,
        width: int = 1300,
        height: int = 500
    ) -> index_imp_vola_and_greeks_IPA_calc:

        self.fig = plotly.subplots.make_subplots(rows=3, cols=1)

        self.fig.add_trace(go.Scatter(
            x=self.IPADf.index, y=self.IPADfGraph.Volatility,
            name='Op Imp Volatility'), row=1, col=1)
        self.fig.add_trace(go.Scatter(
            x=self.IPADf.index, y=self.IPADfGraph.MarketValueInDealCcy,
            name='Op Mk Pr'), row=2, col=1)
        self.fig.add_trace(go.Scatter(
            x=self.IPADf.index, y=self.IPADfGraph.UnderlyingPrice,
            name=self.underlying+' Undrlyg Pr'), row=3, col=1)

        self.fig.update(layout_xaxis_rangeslider_visible=False)
        self.fig.update_layout(title=self.IPADfGraph.columns.name)
        self.fig.update_layout(
            title=self.instrument,
            template=self.graphTemplate,
            autosize=autosize,
            width=width,
            height=height)

        return self

<cell>387: error: Attribute "IPADf" already defined on line 385  [no-redef]


from functools import wraps
from time import time


index_imp_vola_and_greeks_IPA_calc().search_index_opt_ATM().ATMOpt

'STXE41000P3.EX'


# index_imp_vola_and_greeks_IPA_calc().search_index_opt_ATM().IPA_calc().Graph(debug=True).Overlay().fig.show()
s = time()
t1 = index_imp_vola_and_greeks_IPA_calc()
print(time() - s)

0.0


s = time()
t2 = t1.search_index_opt_ATM()
print(time() - s)

4.971007823944092


t1.ATMOpt

'STXE41000P3.EX'


index_imp_vola_and_greeks_IPA_calc().search_index_opt_ATM().ATMOpt

'STXE41250P3.EX'


index_imp_vola_and_greeks_IPA_calc().search_index_opt_ATM().ATMOpt

'STXE41250P3.EX'


s = time()
t3 = t2.IPA_calc()
print(time() - s)

21.338878870010376


s = time()
t3 = t2.Graph(debug=True)
print(time() - s)

0.1254889965057373


s = time()
t4 = t3.Overlay().fig.show()
print(time() - s)

0.0411677360534668


index_imp_vola_and_greeks_IPA_calc().search_index_opt_ATM().IPA_calc().Graph(debug=True).Stack3().fig.show()


index_imp_vola_and_greeks_IPA_calc().search_index_opt_ATM().IPA_calc().Simple_graph().plt.show()

[Error 400 - invalid_grant] empty error description


test1 = index_imp_vola_and_greeks_IPA_calc(index_underlying=".SPX")
test2 = test1.search_index_opt_ATM(
    debug=False,
    after=15,
    call_or_put='Put',
    searchFields=["ExchangeCode", "UnderlyingQuoteName"],
    include_weekly_opts=False,
    topNuSearchResults=10,
    timeOfCalcDatetime=datetime.now(),
    underMrktPriceField="TRDPRC_1")
test3 = test2.IPA_calc(
    dateBack=3,
    optnMrktPriceField="TRDPRC_1",
    debug=False,
    atOptionTradeOnly=True,
    riskFreeRate=None,
    riskFreeRateField=None,
    timeZoneInGraph=datetime.now().astimezone())


test3.IPADf


test4 = test3.Simple_graph(
    maxColwidth=200,
    size=(15, 5),
    lineStyle='.-',  # 'o-'
    plotting='Volatility'
)


test4.plt.show()


# rd.close_session()  # It's good practice to close our RD session when done.


# # !pip3 install --trusted-host pypi.org dash_tvlwc
# # !pip3 install --trusted-host pypi.org dash

# import random

# import dash_tvlwc
# import dash
# from dash.dependencies import Input, Output, State
# from dash import html

# from datetime import datetime
# import random

# import pandas as pd

# def generate_random_series(*args, **kwargs):
#     return generate_random_ohlc(*args, **kwargs, close_only=True)

# def generate_random_ohlc(v0: float, ret=0.05, n=500, t0='2021-01-01', close_only=False):
#     datelist = [dt.strftime('%Y-%m-%d') for dt in pd.date_range(t0, periods=n).tolist()]
#     res = []
#     c = v0
#     for dt in datelist:
#         o = c
#         c = o * (1 + random.uniform(-ret, ret))
#         if not close_only:
#             h = max(o, c) * (1 + random.uniform(0, ret))
#             l = min(o, c) * (1 + random.uniform(-ret, 0))
#             res.append({
#                 "time": dt,
#                 "open": o,
#                 "high": h,
#                 "low": l,
#                 "close": c
#             })
#         else:
#             res.append({
#                 "time": dt,
#                 "value": c
#             })
#         o = c
#     return res

# app = dash.Dash(__name__, external_stylesheets=['./assets/stylesheet.css'])

# chart_options = {
#     'layout': {
#         'background': {'type': 'solid', 'color': '#1B2631'},
#         'textColor': 'white',
#     },
#     'grid': {
#         'vertLines': {'visible': False},
#         'horzLines': {'visible': False},
#     },
#     'localization': {'locale': 'en-US'}
# }

# panel1 = [
#     html.H2('Bar'),
#     dash_tvlwc.Tvlwc(
#         id='bar-chart',
#         seriesData=[generate_random_ohlc(v0=100, n=50)],
#         seriesTypes=['bar'],
#         width='100%',
#         chartOptions=chart_options
#     )
# ]


# p2_series = generate_random_ohlc(v0=1, n=50, ret=0.1)
# p2_series = [{'time': v['time']} if 12 < idx < 20 or idx > 45 else v for idx, v in enumerate(p2_series)]
# panel2 = [
#     html.H2('Candlestick'),
#     dash_tvlwc.Tvlwc(
#         id='candlestick-chart',
#         seriesData=[p2_series],
#         seriesTypes=['candlestick'],
#         seriesOptions=[{
#             'downColor': '#a6269a',
#             'upColor': '#ffaa30',
#             'borderColor': 'black',
#             'wickColor': 'black'
#         }],
#         width='100%',
#         chartOptions={'layout': {'background': {'type': 'solid', 'color': 'white'}}}
#     )
# ]


# panel3 = [
#     html.H2('Area'),
#     dash_tvlwc.Tvlwc(
#         id='area-chart',
#         seriesData=[generate_random_series(v0=15, n=50)],
#         seriesTypes=['area'],
#         seriesOptions=[{
#             'lineColor': '#FFAA30',
#             'topColor': '#2962FF',
#             'bottomColor': 'rgba(180, 98, 200, 0.1)',
#             'priceLineWidth': 3,
#             'priceLineColor': 'red'
#         }],
#         width='100%',
#         chartOptions=chart_options
#     )
# ]


# p4_series = generate_random_series(v0=5000, n=50)
# p4_mean = sum([p['value'] for p in p4_series]) / 50
# p4_max = max([p['value'] for p in p4_series])
# price_lines = [{'price': p4_max, 'color': '#2962FF', 'lineStyle': 0, 'title': 'MAX PRICE', 'axisLabelVisible': True}]
# panel4 = [
#     html.H2('Baseline'),
#     dash_tvlwc.Tvlwc(
#         id='baseline-chart',
#         seriesData=[p4_series],
#         seriesTypes=['baseline'],
#         seriesOptions=[{
#             'baseValue': {'type': 'price', 'price': p4_mean},
#             'topFillColor1': 'black',
#             'topFillColor2': 'rgba(255,255,255,0)',
#             'topLineColor': 'black',
#             'crosshairMarkerRadius': 8,
#             'lineWidth': 5,
#             'priceScaleId': 'left'
#         }],
#         seriesPriceLines=[price_lines],
#         width='100%',
#         chartOptions={
#             'rightPriceScale': {'visible': False},
#             'leftPriceScale': {'visible': True, 'borderColor': 'rgba(197, 203, 206, 1)',},
#             'timeScale': {'visible': False},
#             'grid': {'vertLines': {'visible': False}, 'horzLines': {'style': 0, 'color': 'black'}},
#         }
#     )
# ]


# # add markers and add color to volume bar
# p5_series = generate_random_series(v0=1, n=50, ret=0.1)
# markers = [
#     {'time': p5_series[15]['time'], 'position': 'aboveBar', 'color': '#f68410', 'shape': 'circle', 'text': 'Signal'},
#     {'time': p5_series[20]['time'], 'position': 'belowBar', 'color': 'white', 'shape': 'arrowUp', 'text': 'Buy'}
# ]
# p5_series_volume = generate_random_series(v0=100, n=50, ret=0.05)
# for i in p5_series_volume:
#     i['color'] = random.choice(['rgba(0, 150, 136, 0.8)', 'rgba(255,82,82, 0.8)'])

# panel5 = [
#     html.H2('Line and volume'),
#     dash_tvlwc.Tvlwc(
#         id='line-chart',
#         seriesData=[p5_series, p5_series_volume],
#         seriesTypes=['line', 'histogram'],
#         seriesOptions=[
#             {
#                 'lineWidth': 1
#             },
#             {
#                 'color': '#26a69a',
#                 'priceFormat': {'type': 'volume'},
#                 'priceScaleId': '',
#                 'scaleMargins': {'top': 0.9, 'bottom': 0},
#                 'priceLineVisible': False
#             },
#         ],
#         seriesMarkers=[markers],
#         width='100%',
#         chartOptions=chart_options
#     )
# ]


# p6_series = generate_random_series(v0=100, n=50, ret=0.3)
# for idx, _ in enumerate(p6_series):
#     if idx in [5,12,13,14,20,33,34,46]:
#         p6_series[idx]['color'] = 'white'

# panel6 = [
#     html.H2('Histogram'),
#     dash_tvlwc.Tvlwc(
#         id='histogram-chart',
#         seriesData=[p6_series],
#         seriesTypes=['histogram'],
#         seriesOptions=[{
#             'color': '#ff80cc',
#             'base': 100,
#             'priceLineVisible': False,
#             'lastValueVisible': False
#         }],
#         width='100%',
#         chartOptions={'layout': {'textColor': '#ff80cc', 'background': {'type': 'solid', 'color': 'black'}}}
#     )
# ]

# app.layout = html.Div([
#     html.H1('Chart options and series options'),
#     html.Div(className='container', children=[
#         html.Div(className='one', children=panel1),
#         html.Div(className='two', children=panel2),
#         html.Div(className='three', children=panel3),
#         html.Div(className='four', children=panel4),
#         html.Div(className='five', children=panel5),
#         html.Div(className='six', children=panel6),
#     ])
# ])

# if __name__ == '__main__':
#     app.run_server(debug=True)


# !where python
# import os, signal
# os.kill(os.getpid(), signal.SIGTERM)


# import refinitiv.data as rd
# rd.get_config().set_param(
#     param=f"logs.transports.console.enabled", value=True
# )
# session = rd.open_session("desktop.workspace")
# session.set_log_level("DEBUG")


# SPX_test2OptnMrktPrice10min = rd.get_history(
#     universe=list(SPX_test2['valid_ric'][0].keys())[0],
#     fields=["TRDPRC_1"],
#     interval="10min",
#     start='2021-10-25T11:53:09.168706',
#     end='2021-10-26T19:53:10.166926')  # Ought to always end at 8 pm for OPRA exchanged Options, more info in the article below
# SPX_test2OptnMrktPrice


# SPX_test2OptnMrktPrice1d = rd.content.historical_pricing.summaries.Definition(
#     universe=list(SPX_test2['valid_ric'][0].keys())[0],
#     start='2021-10-23',
#     end='2021-10-29',
#     fields=['BID', 'ASK', 'TRDPRC_1'],
#     interval=rd.content.historical_pricing.Intervals.DAILY).get_data()


SPX_test2OptnMrktPrice1d.data.df


# SPX_test2OptnMrktPriceTest = rd.content.historical_pricing.summaries.Definition(
#     interval=rd.content.historical_pricing.Intervals.FIVE_MINUTES,
# #     interval='PT1H',
#     universe=list(SPX_test2['valid_ric'][0].keys())[0],
#     start='2021-10-25T10:53:09.168706',
#     end='2021-10-27T19:53:10.166926',
#     fields=['BID', 'ASK', 'TRDPRC_1']).get_data()


# SPX_test2OptnMrktPriceTest.errors


# SPX_test2OptnMrktPriceTest.get_data().data.df


# SPX_test2OptnMrktPriceTest


ek = rd.eikon
# The key is placed in a text file so that it may be used in this code without showing it itself:
eikon_key = open("eikon.txt", "r")
ek.set_app_key(str(eikon_key.read()))
# It is best to close the files we opened in order to make sure that we don't stop any other services/programs from accessing them if they need to:
eikon_key.close()


testDf, err = ek.get_data(
    instruments=list(SPX_test2['valid_ric'][0].keys())[0],
    fields=['TRDPRC_1'],
    parameters={
        'SDate': '2021-10-25T10:53:09.168706',
        'EDate': '2021-10-28T19:53:10.166926'})


testDf


def Get_trans_days(year, mcal_get_calendar='EUREX', trans_day='first'):
    '''
    Get_trans_days Version 2.0:

    This function gets transaction days for each month of a specified year.

    Changes
    ----------------------------------------------
    Changed from Version 1.0 to 2.0: Jonathan Legrand changed Haykaz Aramyan's original code to allow
        (i) function name changed from `get_trans_days` to `Get_trans_days`
        (ii) for the function's holiday argument to be changed, allowing for any calendar supported by `mcal.get_calendar` and defaulted to 'EUREX' as opposed to 'CBOE_Index_Options' and

    Dependencies
    ----------------------------------------------
    import datetime.timedelta as timedelta. (This is a native Python library, so it ought to be version '3.8.12'.)
    Python library 'pandas_market_calendars' version '3.2'.
    pandas_market_calendars as mcal version '4.1.0'

    Parameters
    -----------------------------------------------
    Arguments:
        year (int):
            Year for which transaction days are requested

        mcal_get_calendar(str):
            String of the calendar for which holidays have to be taken into account. More on this calendar (link to Github checked 2022-10-11): https://github.com/rsheftel/pandas_market_calendars/blob/177e7922c7df5ad249b0d066b5c9e730a3ee8596/pandas_market_calendars/exchange_calendar_cboe.py
            Default: mcal_get_calendar='EUREX'

        trans_day (str):
            Takes either 'first' or 'third' indicating to the first business day or the 3rd Friday of a month respectively
            Default: trans_day='first'

    Output:
        trans_days (list):
            List of days for 12 month
    '''
    # get the first business day of each month
    if trans_day == 'first':
        mkt = mcal.get_calendar(mcal_get_calendar)
        holidays = mkt.holidays().holidays

        # set start and end day ranges
        start_date, end_date = f"{str(year)}-01-01", f"{str(year)}-12-31"
        trans_days = []

        for date in pd.date_range(start_date, end_date, freq='BMS'):
            # get the first day after the weekend after checking for holiday
            while date.isoweekday() > 5 or date in holidays:
                date += timedelta(1)
            # add found day to the list
            trans_days.append(date.date().day)

    # get the 3rd Friday for each month by calling function "get_exp_dates"
    elif trans_day == 'third':
        trans_days = get_exp_dates(year)[year]
    else:
        print('Please input "first" or "third" for transaction day')
        return
    return trans_days


def Adjustment_factor(corp_event, year=None, date=None, trans_day='first', mcal_get_calendar='EUREX'):
    '''
    Adjustment_factor Version 2.0:

    This function gets adjustment factor(s) of stock split for a given asset. If no split event is happened during the requested period
    function returns 1(if date argument is used) or list of twelve 1s (if year argument is used), which assumes no adjustment in prices.

    Changes
    ----------------------------------------------
    Changed from Version 1.0 to 2.0: Jonathan Legrand changed Haykaz Aramyan's original code:
        (i) function name changed from `adjustment_factor` to `Adjustment_factor`.

    Dependencies
    ----------------------------------------------
    Python library 'pandas_market_calendars' version 3.2

    Parameters
    -----------------------------------------------
    Input:
        asset (str):
            RIC code of the asset
        year (int):
            Year for which stock split events are requested
        date (str with date (YYYY-MM-DD) format):
            Date as of which stock split events are requested
        trans_day (str, default = 'first'):
            Indicates the date of the transaction for get_trans_days function
        mcal_get_calendar(str):
            String of the calendar for which holidays have to be taken into account. More on this calendar (link to Github checked 2022-10-11): https://github.com/rsheftel/pandas_market_calendars/blob/177e7922c7df5ad249b0d066b5c9e730a3ee8596/pandas_market_calendars/exchange_calendar_cboe.py
            Default: mcal_get_calendar='EUREX'
    Output:
        adj_factor (float): This is returned in case of date argument is used. The output is the Adjustment factor after split
        adj_factors(list): This is returned in case of year argument is used. The output is the list of Adjustment factors after split for each month
    '''
    # if there is no stock split corporate event
    if (corp_event is None) or (corp_event['Capital Change Effective Date'][0] is None):
        if year is not None and date is None:
            # return list of 1s if year argument is used
            adj_factors = 12 * [1]
            return adj_factors
        elif date is not None and year is None:
            # return 1 if exact date argument is used
            adj_factor = 1
            return adj_factor
        else:
            print('Either Year or exact date needs to be passed to the function')
    # if there is an event adjustment factor(s) is(are) calculated
    else:
        if year is not None and date is None:  # in case of year argument is used
            # request transaction dates
            trans_days = Get_trans_days(year=year, mcal_get_calendar='EUREX', trans_day=trans_day)
            adj_factors = []
            for i in range(1, 13):
                # get exp_dates and use it as a request date for stock split corporate events
                exp_date = str(year) + '-' + str(i) + '-' + str(trans_days[i - 1])
                # initiate adj_factor with 1
                adj_factor = 1
                # we first check if the expiration date of option is after or before the adjustment date
                for j in reversed(range(len(corp_event))):
                    # if expiration date is smaller than adjustment date then we need adjustment
                    if pd.to_datetime(exp_date).strftime('%Y-%m-%d') < pd.to_datetime(corp_event['Capital Change Effective Date'][j]).strftime('%Y-%m-%d'):
                        adj_factor = float(corp_event['Adjustment Factor'][j]) * adj_factor # we should consider all adjustment factors which are after the expiration day
                # append adjustment factor of the month to the list
                adj_factors.append(adj_factor)
            return adj_factors

        elif date is not None and year is None:  # In case exact date  argument is ued
            adj_factor = 1
            for j in reversed(range(len(corp_event))):
                # if expiration date is smaller than adjustment date then we need adjustment
                if pd.to_datetime(date).strftime('%Y-%m-%d') < corp_event['Capital Change Effective Date'][j]:
                    adj_factor = float(corp_event['Adjustment Factor'][j]) * adj_factor
            return adj_factor
        else:
            print('Either Year or exact date needs to be passed to the function')


def Get_potential_rics(year, trans_day, asset, OTM_size, diff, opt_type, reportLog=True, debug=False):
    '''
    Get_potential_rics Version 2.0:
    This function returns the list of potential option RICs for a specified year reconstructed based on Refinitiv RIC and option trading rules.

    Changes
    ----------------------------------------------
    Changed from Version 1.0 to 2.0: Jonathan Legrand changed Haykaz Aramyan's original code:
        (i) function name changed from `get_potential_rics` to `Get_potential_rics`
        (ii) changed function body to reflect changed funciton name from `get_trans_days` to `Get_trans_days`
        (iii) added argument `reportLog`, a boolean value that, if left to default `True`, report log of the function output

    Dependencies
    ----------------------------------------------
    Python library 'Refinitiv Dataplatform' version 1.0.0a8.post1

    Parameters
    -----------------------------------------------
    Input:
        year (int): year for which transaction days are requested
        trans_day (str, default = 'first'): takes either 'first' or 'third' indicating to the first business day or the 3rd Friday of a month respectively
        asset (str): RIC code of the asset
        OTM_size (int): percentage number indicating how far away is the strike price from the price of the underlying asset
        diff (int): Tolarated difference in OTM to construct upper and lower bounds of strike prices
        opt_type (str): takes either "call" or "put"
        reportLog (bool): report log of the function output if True. Default: reportLog=True.

    Output:
        Tuple of two objects:
            potential_RICs (dict): dictionary containing potential RICs for each month with strike prices from the lower to upper bounds of strikes
            strikes (list): list of the strike prices calculated based on OTM size for each month
    '''

    # open file to report log of the function output
    if reportLog:
        report = open("Log report.txt", "a")

    # call functions to get expiration and transaction days
    trans_days = Get_trans_days(
        year=year, mcal_get_calendar='EUREX', trans_day=trans_day)
    trans_days_prev = Get_trans_days(
        year=year-1, mcal_get_calendar='EUREX', trans_day=trans_day)
    dates = get_exp_dates(year)

    # trim underlying asset's RIC to get the required part for option RIC
    if asset[0] == '.':  # check if the asset is an index or an equity
        asset_name = asset[1:]  # get the asset name - we remove "." symbol for index options
        adj_factors = 12 * [1]  # set adjustment factors to be equal to 1 for each month (no stock split corporate event is applicable to indices)
    else:
        asset_name = asset.split('.')[0]  # we need only the first part of the RICs for equities
        # get list of corporate events for equities
        if debug: print(f"asset: {asset}")
        corp_event = rd.get_data(
            universe=asset,
            fields=["TR.CAEffectiveDate", "TR.CAAdjustmentFactor", "TR.CAAdjustmentType"],
            parameters={
                "CAEventType": "SSP",
                "SDate": datetime.today().strftime("%Y-%m-%d"),
                "EDate": "-50Y"})
        if debug: print(f"corp_event: {corp_event}")
        # run adjustment_factor function to get the factors
        adj_factors = Adjustment_factor(corp_event, year=year, trans_day=trans_day)

    # define expiration month codes to be used after "^" sign
    exp = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L']
    potential_RICs = {}
    strikes = []

    # construct potential RICs for each month of a specified year
    for j in range(1, 13):
        # get day of expiration for a month
        day = dates[year][j - 1]
        # get date of price request, which is in the previous month of expiration 
        if j != 1:
            date = str(year) + '-' + str(j - 1) + '-' + str(trans_days[j - 2])
        if j == 1:  # for January, we need to subtract a year along with the month
            date = str(year - 1) + '-' + str(j + 11) + '-' + str(trans_days_prev[j + 10])
        # get price of underlying asset as of the transaction date

        # get the corresponding adjustment factor for the month
        adj_factor = adj_factors[j-1]

        price = rd.get_data(
            asset,
            fields=['TR.PriceClose'],
            parameters={'SDate': date})
        price = float(price.iloc[0, 1]) / adj_factor  # adjust prices by the adjustment factor. if no sptick split events adj_factor = 1

        # calculate the strike price for call options
        if opt_type.lower() == 'call':
            strike = price + price * OTM_size / 100
            # define expiration month codes for call options while also considering the strike price
            if strike > 999.999:
                exp_codes_call = [
                    'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l']
            else:
                exp_codes_call = [
                    'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L']
            # get expiration month code for a month
            exp_month = exp_codes_call[j-1]

        # calculate the strike price and get expiration month code for a month for put options
        elif opt_type.lower() == 'put':
            strike = price - price * OTM_size/100
            if strike > 999.999:
                exp_codes_put = [
                    'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x']
            else:
                exp_codes_put = [
                    'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X']
            exp_month = exp_codes_put[j-1]

        strikes.append(int(round(strike, 0)))  # append the calculated strike price to the list of strikes

        # calculate lower and upper bounds for strikes considering the value of the strike
        if strike > 999.999:
            step = 5  # we loop over strikes with a step 5 for larger strikes
            strike_ub = int(round((strike + strike * diff / 100),-1))
            strike_lb = int(round((strike - strike * diff / 100),-1))
        else:
            step = 1  # we loop over strikes with a step 1 for smaller strikes
            strike_ub = int(strike + strike * diff / 100)
            strike_lb = int(strike - strike * diff / 100)

        # construct RICs for each strike from the lower to upper bound ranges of strikes
        for n in range(strike_lb, strike_ub + step, step):
            k = None  # for strikes < 1000 along with 1 step increment change in strikes we do 0.5 point increment change which 
                      # allows us to consider strikes with decimal points. This is important to get closer OTMs for smaller valued assets.
            # here we construct option RICs by adding together all the RIC components
            # Please note some of the components are different depending on the strike value
            plc_holdr1 = asset_name + exp_month + str(day) + str(year)[-2:]
            plc_holdr2 = exp[j - 1] + str(year)[-2:]
            if n < 10:
                z = plc_holdr1 + '00' + str(n) + '00.U^' + plc_holdr2# for integer steps
                k = plc_holdr1 + '00' + str(n) + '50.U^' + plc_holdr2# for decimal steps
            elif n >= 10 and n < 100:
                z = plc_holdr1 + '0' + str(n) + '00.U^' + plc_holdr2
                k = plc_holdr1 + '0' + str(n) + '50.U^' + plc_holdr2
            if n >= 100 and n < 1000:
                z = plc_holdr1 + str(n) + '00.U^' + plc_holdr2  
                k = plc_holdr1 + str(n) + '50.U^' + plc_holdr2
            elif n >= 1000 and n < 10000:
                z = plc_holdr1 + str(n) + '0.U^' + plc_holdr2
            elif n >= 10000 and n < 20000:
                z = plc_holdr1 + 'A' + str(n)[-4:] + '.U^' + plc_holdr2
            elif n >= 20000 and n < 30000:
                z = plc_holdr1 + 'B' + str(n)[-4:] + '.U^' + plc_holdr2
            elif n >= 30000 and n < 40000:
                z = plc_holdr1 + 'C' + str(n)[-4:] + '.U^' + plc_holdr2
            elif n >= 40000 and n < 50000:
                z = plc_holdr1 + 'D' + str(n)[-4:] + '.U^' + plc_holdr2

            # append RICs with integer strikes to the dictionary
            if j in potential_RICs:
                potential_RICs[j].append(z)
                # append RICs with decimal point strikes to the dictionary
                if k is not None:
                    potential_RICs[j].append(k)
            else:
                potential_RICs[j] = [z]
                if k is not None:
                    potential_RICs[j].append(k)

    # report funtion results and close the log file
    if reportLog:
        now = {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
        report.write(f'{now}: Potential RICs for {opt_type} options with {OTM_size}% OTM for {year} are constructed\n')
        report.close()
    return potential_RICs, strikes


call_RICs, call_strikes = Get_potential_rics(
    year=2017,
    trans_day='first',
    asset='.STOXX50E',
    OTM_size=5,
    diff=3,
    opt_type='call',
    reportLog=True,
    debug=False)


print(call_strikes)


rd.get_data(
    '.STOXX50E',
    fields=['TR.CAAdjustmentFactor(SDate=2017-01-01,EDate=2018-12-31)'])


import refinitiv.data as rd
import refinitiv.data.content.ipa.financial_contracts as rdf
from refinitiv.data.content.ipa.financial_contracts import option
import pandas as pd

# Let's authenticate ourseves to LSEG's Data and Analytics service, Refinitiv:
try:  # The following libraries are not available in Codebook, thus this try loop
    rd.open_session(config_name="C:\\Example.DataLibrary.Python-main\\Example.DataLibrary.Python-main\\Configuration\\refinitiv-data.config.json")
    rd.open_session("desktop.workspace")
except:
    rd.open_session()

[2023-03-28 18:31:23,920] - [DEBUG] - [sessions.platform.rdp.4] - [2976] | MainThread
 + Session created: PlatformSession 
		name = rdp
		server_mode = False
		stream_auto_reconnection = True
		signon_control = True
		authentication_token_endpoint_url = https://api.refinitiv.com/auth/oauth2/v1/token
[2023-03-28 18:31:23,922] - [DEBUG] - [sessions.platform.rdp.4] - [2976] | MainThread
Open session
[2023-03-28 18:31:23,976] - [DEBUG] - [sessions.platform.rdp.4] - [2976] | MainThread
Created session connection SessionCxnType.REFINITIV_DATA
[2023-03-28 18:31:23,978] - [DEBUG] - [sessions.platform.rdp.4] - [2976] | MainThread
AuthManager: start authorize
[2023-03-28 18:31:23,980] - [DEBUG] - [sessions.platform.rdp.4] - [33140] | AuthManager-Thread
AuthManager: Access token will be requested in 1e-05 seconds
[2023-03-28 18:31:24,002] - [DEBUG] - [sessions.platform.rdp.4] - [33140] | AuthManager-Thread
Request to https://api.refinitiv.com/auth/oauth2/v1/token
	method = POST
	headers = {'Accept': 'application/json', 'x-tr-applicationid': '383058f500e44980a576a7e02b0b187f8856e7ac'}
	params = None
	cookies = None
	data = {'scope': 'trapi', 'grant_type': 'password', 'username': 'GE-A-01103867-3-7874', 'password': ********, 'takeExclusiveSignOnControl': 'true', 'client_id': '383058f500e44980a576a7e02b0b187f8856e7ac'}
	json = None
[2023-03-28 18:31:24,833] - [DEBUG] - [sessions.platform.rdp.4] - [33140] | AuthManager-Thread
HTTP request response 200: { "access_token":"eyJ0eXAiOiJhdCtqd3QiLCJhbGciOiJSUzI1NiIsImtpZCI6ImRMdFd2Q0tCSC1NclVyWm9YMXFod2pZQ2t1eDV0V2ZSS2o4ME9vcjdUY28ifQ.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.He3VKCIicLFn9AdC6LEkmNW-8-C9dfscpDZnJjn_7Z-7D6vtzfVeYPBcOHMgqQX44b8QK2H_yP5-5eXLzYp41kVq1TrSxjui9TJn56ukLkB330pTpR85uJbepgxPFRLLj8pYy1FqWWzcpSUtiz7FE1ZNGpz85NZJx44KrTkkCGyO-gnTdltRb3s7UWy0LN3LGmZ0fQDPIk4HMUV2SXRzfpnsu54FbgR8aM03w_L-D4_VXCNLpidAddNs9R2YzafyNnSquCZ3BN7xKGuePCfiRxnPYde5d__vCvOF7we5nZeisl5HhUVW3q7_xf6lgOT3A3vrWRlgFVcdw766MXnC5w", "refresh_token":"b188a7f6-1c83-4cf6-9c7b-056a8a4d7c0d", "expires_in":"600" , "scope":"trapi.admin.accounts trapi.admin.accounts.groups trapi.admin.accounts.licenses trapi.admin.accounts.users trapi.alerts.history.crud trapi.alerts.news-headlines-1h.crud trapi.alerts.news-headlines-24h.crud trapi.alerts.news-headlines.crud trapi.alerts.news-stories.crud trapi.alerts.news.crud trapi.alerts.preferences.crud trapi.alerts.publication.crud trapi.alerts.research.crud trapi.alerts.subscription.crud trapi.auth.cloud-credentials trapi.autosuggest.metadata.read trapi.autosuggest.read trapi.cfs.claimcheck.read trapi.cfs.claimcheck.write trapi.cfs.publisher.read trapi.cfs.publisher.setup.write trapi.cfs.publisher.stream.write trapi.cfs.publisher.write trapi.cfs.subscriber.read trapi.data.3pri.test.read trapi.data.aggregates.read trapi.data.api.test trapi.data.average-volume-analytics.ava_read trapi.data.benchmark.bmk_read trapi.data.esg.bulk.read trapi.data.esg.metadata.read trapi.data.esg.read trapi.data.esg.universe.read trapi.data.esg.views-basic.read trapi.data.esg.views-measures-full.read trapi.data.esg.views-measures-standard.read trapi.data.esg.views-measures.read trapi.data.esg.views-scores-full.read trapi.data.esg.views-scores-standard.read trapi.data.esg.views-scores.read trapi.data.filings.metadata trapi.data.filings.retrieval trapi.data.filings.search trapi.data.funds.assets.read trapi.data.historical-pricing.events.read trapi.data.historical-pricing.read trapi.data.historical-pricing.summaries.read trapi.data.ms-analytics.bond-liquidity-starmine trapi.data.news-story-viewer.read trapi.data.news.read trapi.data.ownership.adv trapi.data.ownership.basic trapi.data.portfolios.read trapi.data.portfolios.write trapi.data.pricing.read trapi.data.quantitative-analytics.read trapi.data.research.read trapi.data.symbology.advanced.read trapi.data.symbology.bulk.read trapi.data.symbology.read trapi.data.trading-analytics.reporting.fitca trapi.data.trading-analytics.reporting.rts27 trapi.data.trading-analytics.reporting.rts28 trapi.data.trading-analytics.reporting.si trapi.data.trading-analytics.tds.history.fix trapi.data.trading-analytics.tds.history.redi trapi.data.trading-analytics.tds.report.redi trapi.data.transactions.qualid.read trapi.data.verify.document.qualid.read trapi.data.verify.identity.qualid.read trapi.data.verify.idv.business.read trapi.data.verify.liveness.qualid.read trapi.data.verify.screening.qualid.read trapi.database-subscriptions.crud trapi.database-subscriptions.users.crud trapi.deployment.crud trapi.graphql.subscriber.access trapi.hvmi.prof.crud trapi.hvmi.prof.read trapi.hvmi.ret.crud trapi.hvmi.ret.read trapi.metadata.cdfsto.read trapi.metadata.cdfsto.write trapi.metadata.nsa.read trapi.metadata.read trapi.metadata.write trapi.msg-svcs.eod-pricing.crud trapi.msg-svcs.readership-company.crud trapi.msg-svcs.readership-story.crud trapi.platform.iam.accounts trapi.platform.iam.app_id trapi.platform.iam.bulk-transactions trapi.platform.iam.guiss trapi.platform.iam.product-maintenance trapi.platform.iam.users trapi.platform.license-management trapi.platform.license.mgmt trapi.platform.reports trapi.platform.reports.admin trapi.platform.reports.permissions trapi.platform.reports.usage trapi.rsa.lowlevel.read trapi.rsa.lowlevel.write trapi.search.explore.read trapi.search.lookup.read trapi.search.metadata.read trapi.search.read trapi.searchcore.lookup.read trapi.searchcore.metadata.read trapi.searchcore.read trapi.searchlite.lookup.read trapi.searchlite.metadata.read trapi.searchlite.read trapi.serviceinsight.access trapi.streaming.prcperf.read trapi.streaming.pricing.read trapi.streaming.synthetic.read trapi.streaming.trading-analytics.tds.provider.redi trapi.streaming.trading-analytics.tds.stream.fix trapi.streaming.trading-analytics.tds.stream.redi trapi.synthetic.crud trapi.trading.auctions trapi.transfer-job.ctrl trapi.user-framework.application-metadata.raplib trapi.user-framework.mobile.crud trapi.user-framework.recently-used.crud trapi.user-framework.workspace.crud trapi.userdata.lists.read trapi.userdata.lists.write trapi.wcng.dug.read trapi.wealth.clientdata trapi.wealth.org.reference trapi.wealth.plaid", "token_type":"Bearer" }  
[2023-03-28 18:31:24,834] - [DEBUG] - [sessions.platform.rdp.4] - [33140] | AuthManager-Thread
Latency: 0.8320982456207275 sec.
Access token response: { "access_token":"eyJ0eXAiOiJhdCtqd3QiLCJhbGciOiJSUzI1NiIsImtpZCI6ImRMdFd2Q0tCSC1NclVyWm9YMXFod2pZQ2t1eDV0V2ZSS2o4ME9vcjdUY28ifQ.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.He3VKCIicLFn9AdC6LEkmNW-8-C9dfscpDZnJjn_7Z-7D6vtzfVeYPBcOHMgqQX44b8QK2H_yP5-5eXLzYp41kVq1TrSxjui9TJn56ukLkB330pTpR85uJbepgxPFRLLj8pYy1FqWWzcpSUtiz7FE1ZNGpz85NZJx44KrTkkCGyO-gnTdltRb3s7UWy0LN3LGmZ0fQDPIk4HMUV2SXRzfpnsu54FbgR8aM03w_L-D4_VXCNLpidAddNs9R2YzafyNnSquCZ3BN7xKGuePCfiRxnPYde5d__vCvOF7we5nZeisl5HhUVW3q7_xf6lgOT3A3vrWRlgFVcdw766MXnC5w", "refresh_token":"b188a7f6-1c83-4cf6-9c7b-056a8a4d7c0d", "expires_in":"600" , "scope":"trapi.admin.accounts trapi.admin.accounts.groups trapi.admin.accounts.licenses trapi.admin.accounts.users trapi.alerts.history.crud trapi.alerts.news-headlines-1h.crud trapi.alerts.news-headlines-24h.crud trapi.alerts.news-headlines.crud trapi.alerts.news-stories.crud trapi.alerts.news.crud trapi.alerts.preferences.crud trapi.alerts.publication.crud trapi.alerts.research.crud trapi.alerts.subscription.crud trapi.auth.cloud-credentials trapi.autosuggest.metadata.read trapi.autosuggest.read trapi.cfs.claimcheck.read trapi.cfs.claimcheck.write trapi.cfs.publisher.read trapi.cfs.publisher.setup.write trapi.cfs.publisher.stream.write trapi.cfs.publisher.write trapi.cfs.subscriber.read trapi.data.3pri.test.read trapi.data.aggregates.read trapi.data.api.test trapi.data.average-volume-analytics.ava_read trapi.data.benchmark.bmk_read trapi.data.esg.bulk.read trapi.data.esg.metadata.read trapi.data.esg.read trapi.data.esg.universe.read trapi.data.esg.views-basic.read trapi.data.esg.views-measures-full.read trapi.data.esg.views-measures-standard.read trapi.data.esg.views-measures.read trapi.data.esg.views-scores-full.read trapi.data.esg.views-scores-standard.read trapi.data.esg.views-scores.read trapi.data.filings.metadata trapi.data.filings.retrieval trapi.data.filings.search trapi.data.funds.assets.read trapi.data.historical-pricing.events.read trapi.data.historical-pricing.read trapi.data.historical-pricing.summaries.read trapi.data.ms-analytics.bond-liquidity-starmine trapi.data.news-story-viewer.read trapi.data.news.read trapi.data.ownership.adv trapi.data.ownership.basic trapi.data.portfolios.read trapi.data.portfolios.write trapi.data.pricing.read trapi.data.quantitative-analytics.read trapi.data.research.read trapi.data.symbology.advanced.read trapi.data.symbology.bulk.read trapi.data.symbology.read trapi.data.trading-analytics.reporting.fitca trapi.data.trading-analytics.reporting.rts27 trapi.data.trading-analytics.reporting.rts28 trapi.data.trading-analytics.reporting.si trapi.data.trading-analytics.tds.history.fix trapi.data.trading-analytics.tds.history.redi trapi.data.trading-analytics.tds.report.redi trapi.data.transactions.qualid.read trapi.data.verify.document.qualid.read trapi.data.verify.identity.qualid.read trapi.data.verify.idv.business.read trapi.data.verify.liveness.qualid.read trapi.data.verify.screening.qualid.read trapi.database-subscriptions.crud trapi.database-subscriptions.users.crud trapi.deployment.crud trapi.graphql.subscriber.access trapi.hvmi.prof.crud trapi.hvmi.prof.read trapi.hvmi.ret.crud trapi.hvmi.ret.read trapi.metadata.cdfsto.read trapi.metadata.cdfsto.write trapi.metadata.nsa.read trapi.metadata.read trapi.metadata.write trapi.msg-svcs.eod-pricing.crud trapi.msg-svcs.readership-company.crud trapi.msg-svcs.readership-story.crud trapi.platform.iam.accounts trapi.platform.iam.app_id trapi.platform.iam.bulk-transactions trapi.platform.iam.guiss trapi.platform.iam.product-maintenance trapi.platform.iam.users trapi.platform.license-management trapi.platform.license.mgmt trapi.platform.reports trapi.platform.reports.admin trapi.platform.reports.permissions trapi.platform.reports.usage trapi.rsa.lowlevel.read trapi.rsa.lowlevel.write trapi.search.explore.read trapi.search.lookup.read trapi.search.metadata.read trapi.search.read trapi.searchcore.lookup.read trapi.searchcore.metadata.read trapi.searchcore.read trapi.searchlite.lookup.read trapi.searchlite.metadata.read trapi.searchlite.read trapi.serviceinsight.access trapi.streaming.prcperf.read trapi.streaming.pricing.read trapi.streaming.synthetic.read trapi.streaming.trading-analytics.tds.provider.redi trapi.streaming.trading-analytics.tds.stream.fix trapi.streaming.trading-analytics.tds.stream.redi trapi.synthetic.crud trapi.trading.auctions trapi.transfer-job.ctrl trapi.user-framework.application-metadata.raplib trapi.user-framework.mobile.crud trapi.user-framework.recently-used.crud trapi.user-framework.workspace.crud trapi.userdata.lists.read trapi.userdata.lists.write trapi.wcng.dug.read trapi.wealth.clientdata trapi.wealth.org.reference trapi.wealth.plaid", "token_type":"Bearer" }  
[2023-03-28 18:31:24,836] - [DEBUG] - [sessions.platform.rdp.4] - [33140] | AuthManager-Thread
AuthManager: Access token handler, event: access_token_success, message: All is well
[2023-03-28 18:31:24,837] - [DEBUG] - [sessions.platform.rdp.4] - [33140] | AuthManager-Thread
Access token eyJ0eXAiOiJhdCtqd3QiLCJhbGciOiJSUzI1NiIsImtpZCI6ImRMdFd2Q0tCSC1NclVyWm9YMXFod2pZQ2t1eDV0V2ZSS2o4ME9vcjdUY28ifQ.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.He3VKCIicLFn9AdC6LEkmNW-8-C9dfscpDZnJjn_7Z-7D6vtzfVeYPBcOHMgqQX44b8QK2H_yP5-5eXLzYp41kVq1TrSxjui9TJn56ukLkB330pTpR85uJbepgxPFRLLj8pYy1FqWWzcpSUtiz7FE1ZNGpz85NZJx44KrTkkCGyO-gnTdltRb3s7UWy0LN3LGmZ0fQDPIk4HMUV2SXRzfpnsu54FbgR8aM03w_L-D4_VXCNLpidAddNs9R2YzafyNnSquCZ3BN7xKGuePCfiRxnPYde5d__vCvOF7we5nZeisl5HhUVW3q7_xf6lgOT3A3vrWRlgFVcdw766MXnC5w. Expire in 600.0 seconds
[2023-03-28 18:31:24,838] - [DEBUG] - [sessions.platform.rdp.4] - [33140] | AuthManager-Thread
AuthManager: Refresh token will be requested in 299 seconds
[2023-03-28 18:31:24,838] - [DEBUG] - [sessions.platform.rdp.4] - [2976] | MainThread
AuthManager: end authorize, result True
[2023-03-28 18:31:24,841] - [DEBUG] - [sessions.platform.rdp.4] - [2976] | MainThread
Opened session
[2023-03-28 18:31:24,847] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
DesktopSession created with following parameters: app_key="9afd663988954069a4c4140d944e01c3a568e9a9", name="workspace" base_url="http://localhost:9000" platform_path_rdp="/api/rdp" platform_path_udf="/api/udf" handshake_url="/api/handshake"
[2023-03-28 18:31:24,849] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
 + Session created: <refinitiv.data.session.Definition object at 0x27020df0 {name='workspace'}>
[2023-03-28 18:31:24,850] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
Open session
[2023-03-28 18:31:24,896] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
Created session connection SessionCxnType.DESKTOP
[2023-03-28 18:31:24,902] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
Request to http://localhost:9001/api/status
	method = GET
	headers = {'x-tr-applicationid': '9afd663988954069a4c4140d944e01c3a568e9a9'}
	params = None
	cookies = None
	data = None
	json = None
[2023-03-28 18:31:26,969] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
HTTP request response 200: {"statusCode":"ST_PROXY_READY","version":"3.4.2"}
[2023-03-28 18:31:26,971] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
Checking proxy url http://localhost:9001/api/status response : 200 - {"statusCode":"ST_PROXY_READY","version":"3.4.2"}
[2023-03-28 18:31:26,972] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
Port 9001 was retrieved from .portInUse file
[2023-03-28 18:31:26,973] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
Try to handshake on url http://localhost:9001/api/handshake...
[2023-03-28 18:31:26,974] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
Request to http://localhost:9001/api/handshake
	method = POST
	headers = {'Content-Type': 'application/json', 'x-tr-applicationid': '9afd663988954069a4c4140d944e01c3a568e9a9'}
	params = None
	cookies = None
	data = None
	json = {'AppKey': '9afd663988954069a4c4140d944e01c3a568e9a9', 'AppScope': 'trapi', 'ApiVersion': '1', 'LibraryName': 'RDP Python Library', 'LibraryVersion': '1.0.0b24'}
[2023-03-28 18:31:26,983] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
HTTP request response 200: {"access_token":"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJBcGlWZXJzaW9uIjoiMSIsIkFwcEtleSI6IjlhZmQ2NjM5ODg5NTQwNjlhNGM0MTQwZDk0NGUwMWMzYTU2OGU5YTkiLCJBcHBTY29wZSI6InRyYXBpIiwiTGlicmFyeU5hbWUiOiJSRFAgUHl0aG9uIExpYnJhcnkiLCJMaWJyYXJ5VmVyc2lvbiI6IjEuMC4wYjI0IiwiaWF0IjoxNjgwMDIxMDg2LCJleHAiOjE2ODEyMzA2ODZ9.1zMH_dZuCxyMKpxq2xG3CkJ2jTGmXUB6qkSQu2BCc1o","expires_in":1209600,"token_type":"bearer"}
[2023-03-28 18:31:26,984] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
Response : 200 - {"access_token":"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJBcGlWZXJzaW9uIjoiMSIsIkFwcEtleSI6IjlhZmQ2NjM5ODg5NTQwNjlhNGM0MTQwZDk0NGUwMWMzYTU2OGU5YTkiLCJBcHBTY29wZSI6InRyYXBpIiwiTGlicmFyeU5hbWUiOiJSRFAgUHl0aG9uIExpYnJhcnkiLCJMaWJyYXJ5VmVyc2lvbiI6IjEuMC4wYjI0IiwiaWF0IjoxNjgwMDIxMDg2LCJleHAiOjE2ODEyMzA2ODZ9.1zMH_dZuCxyMKpxq2xG3CkJ2jTGmXUB6qkSQu2BCc1o","expires_in":1209600,"token_type":"bearer"}
[2023-03-28 18:31:26,986] - [DEBUG] - [sessions.desktop.workspace.5] - [2976] | MainThread
Opened session


def Chunks(lst, n):
    """Yield successive n-sized chunks from lst."""
    for i in range(0, len(lst), n):
        yield lst[i:i + n]


HSI_test0 = rd.content.historical_pricing.summaries.Definition(
    'STXE42000D3.EX',
    interval=rd.content.historical_pricing.Intervals.DAILY,
    fields=['SETTLE'],
    start='2022-11-07',
    end='2023-02-01').get_data().data.df
hk_rf = 100 - rd.get_history(
    universe=['HK3MT=RR'],  # HK10YGB=EODF, HKGOV3MZ=R, HK3MT=RR
    fields=['TR.MIDPRICE'],
    start=HSI_test0.index[0].strftime('%Y-%m-%d'),
    end=HSI_test0.index[-1].strftime('%Y-%m-%d'))
HSI_test1 = pd.merge(
    HSI_test0, hk_rf, left_index=True, right_index=True)
HSI_test1 = HSI_test1.rename(
    columns={"SETTLE": "OptionPrice", "Mid Price": "RfRatePrct"})
hist_HSI_undrlying_pr = rd.get_history(
    universe=['.HSI'],
    fields=["TRDPRC_1"],
    # interval="1D",
    start=HSI_test0.index[0].strftime('%Y-%m-%d'),
    end=HSI_test0.index[-1].strftime('%Y-%m-%d'))
HSI_test2 = pd.merge(HSI_test1, hist_HSI_undrlying_pr,
                    left_index=True, right_index=True)
HSI_test2 = HSI_test2.rename(
    columns={"TRDPRC_1": "UndrlyingPr"})
HSI_test2.columns.name = 'STXE42000D3.EX'
HSI_test2


requestFields = ['MarketValueInDealCcy', 'RiskFreeRatePercent', 'UnderlyingPrice', 'PricingModelType', 'DividendType', 'UnderlyingTimeStamp', 'ReportCcy', 'VolatilityType', 'Volatility', 'DeltaPercent', 'GammaPercent', 'RhoPercent', 'ThetaPercent', 'VegaPercent']


live_universe = [
        {
          "instrumentType": "Option",
          "instrumentDefinition": {
            "buySell": "Buy",
            "underlyingType": "Eti",
            "instrumentCode": 'STXE42000D3.EX',
            "strike": float(4200),
          },
          "pricingParameters": {
            "marketValueInDealCcy": float(HSI_test2['OptionPrice'][i]),
            "riskFreeRatePercent": float(HSI_test2['RfRatePrct'][i]),
            "underlyingPrice": float(HSI_test2['UndrlyingPr'][i]),
            "pricingModelType": "BlackScholes",
            "dividendType": "ImpliedYield",
            "volatilityType": "Implied",
            "underlyingTimeStamp": "Default",
            "reportCcy": "HKD"
          }
        }
      for i in range(len(HSI_test2.index))]


batchOf = 100
for i, j in enumerate(Chunks(live_universe, 100)):
    print(f"Batch of {batchOf} requests no. {str(i+1)}/{str(len([i for i in Chunks(live_universe, batchOf)]))} started")
    # Example request with Body Parameter - Symbology Lookup
    live_troubleshoot_request_definition = rd.delivery.endpoint_request.Definition(
        method=rd.delivery.endpoint_request.RequestMethod.POST,
        url='https://api.refinitiv.com/data/quantitative-analytics/v1/financial-contracts',
        body_parameters={"fields": requestFields,
                         "outputs": ["Data", "Headers"],
                         "universe": j})

    live_troubleshoot_resp = live_troubleshoot_request_definition.get_data()
    headers_name = [h['name'] for h in live_troubleshoot_resp.data.raw['headers']]

    if i == 0:
        live_troubleshoot_df = pd.DataFrame(
            data=live_troubleshoot_resp.data.raw['data'],
            columns=headers_name)
    else:
        _live_troubleshoot_df = pd.DataFrame(
            data=live_troubleshoot_resp.data.raw['data'],
            columns=headers_name)
        live_troubleshoot_df = live_troubleshoot_df.append(_live_troubleshoot_df, ignore_index=True)
    print(f"Batch of {batchOf} requests no. {str(i+1)}/{str(len([i for i in Chunks(live_universe, batchOf)]))} ended")

Batch of 100 requests no. 1/1 started
Batch of 100 requests no. 1/1 ended


live_troubleshoot_df


hist_universe = [
        {
          "instrumentType": "Option",
          "instrumentDefinition": {
            "buySell": "Buy",
            "underlyingType": "Eti",
            "instrumentCode": 'HSI19300N3.HF^B23',
            "strike": float(4200),
          },
          "pricingParameters": {
            "marketValueInDealCcy": float(HSI_test2['OptionPrice'][i]),
            "riskFreeRatePercent": float(HSI_test2['RfRatePrct'][i]),
            "underlyingPrice": float(HSI_test2['UndrlyingPr'][i]),
            "pricingModelType": "BlackScholes",
            "dividendType": "ImpliedYield",
            "volatilityType": "Implied",
            "underlyingTimeStamp": "Default",
            "reportCcy": "HKD"
          }
        }
      for i in range(len(HSI_test2.index))]


for i, j in enumerate(Chunks(hist_universe, 100)):
    print(f"Batch of {batchOf} requests no. {str(i+1)}/{str(len([i for i in Chunks(hist_universe, batchOf)]))} started")
    # Example request with Body Parameter - Symbology Lookup
    hist_troubleshoot_request_definition = rd.delivery.endpoint_request.Definition(
        method=rd.delivery.endpoint_request.RequestMethod.POST,
        url='https://api.refinitiv.com/data/quantitative-analytics/v1/financial-contracts',
        body_parameters={"fields": requestFields,
                         "outputs": ["Data", "Headers"],
                         "universe": j})

    hist_troubleshoot_resp = hist_troubleshoot_request_definition.get_data()
    headers_name = [h['name'] for h in hist_troubleshoot_resp.data.raw['headers']]

    if i == 0:
        hist_troubleshoot_df = pd.DataFrame(
            data=hist_troubleshoot_resp.data.raw['data'],
            columns=headers_name)
    else:
        _hist_troubleshoot_df = pd.DataFrame(
            data=hist_troubleshoot_resp.data.raw['data'],
            columns=headers_name)
        hist_troubleshoot_df = hist_troubleshoot_df.append(_hist_troubleshoot_df, ignore_index=True)
    print(f"Batch of {batchOf} requests no. {str(i+1)}/{str(len([i for i in Chunks(hist_universe, batchOf)]))} ended")

Batch of 100 requests no. 1/1 started
Batch of 100 requests no. 1/1 ended


hist_troubleshoot_df


live_hist_daily_universe_l = [
    option.Definition(
        underlying_type=option.UnderlyingType.ETI,
        buy_sell='Buy',
        instrument_code='STXE42000D3.EX',  # 'STXE42000D3.EX' #  'HSI19300N3.HF^B23',  # list(HSI_test2['valid_ric'][0].keys())[0],
        strike=float(4200),
        pricing_parameters=option.PricingParameters(
            market_value_in_deal_ccy=float(HSI_test2['OptionPrice'][i]),
            risk_free_rate_percent=float(HSI_test2['RfRatePrct'][i]),
            underlying_price=float(HSI_test2['UndrlyingPr'][i]),
            pricing_model_type='BlackScholes',
            volatility_type='Implied',
            underlying_time_stamp='Default',
            report_ccy='HKD'
        ))
    for i in range(len(HSI_test2.index))]


for i, j in enumerate(Chunks(live_hist_daily_universe_l, 100)):
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(hist_daily_universe_l, 100)])} started")
    # Example request with Body Parameter - Symbology Lookup
    troubleshoot_resp_live = rdf.Definitions(universe=j, fields=requestFields)
    troubleshoot_resp_live_getdata = troubleshoot_resp_live.get_data()
    if i == 0:
        troubleshoot_resp_live_df = troubleshoot_resp_live_getdata.data.df
    else:
        troubleshoot_resp_live_df = troubleshoot_resp_live_df.append(
            troubleshoot_resp_live_getdata.data.df, ignore_index=True)
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(hist_daily_universe_l, 100)])} ended")

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [18], in <cell line: 1>()
      1 for i, j in enumerate(Chunks(live_hist_daily_universe_l, 100)):
----> 2     print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(hist_daily_universe_l, 100)])} started")
      3     # Example request with Body Parameter - Symbology Lookup
      4     troubleshoot_resp_live = rdf.Definitions(universe=j, fields=requestFields)

NameError: name 'hist_daily_universe_l' is not defined


troubleshoot_resp_live_df


exp_hist_daily_universe_l = [
    option.Definition(
        underlying_type=option.UnderlyingType.ETI,
        buy_sell='Buy',
        instrument_code='HSI19300N3.HF^B23',  # 'STXE42000D3.EX' #  'HSI19300N3.HF^B23'
        strike=float(hist_opt_found_strk_pr),
        pricing_parameters=option.PricingParameters(
            market_value_in_deal_ccy=float(HSI_test2['OptionPrice'][i]),
            risk_free_rate_percent=float(HSI_test2['RfRatePrct'][i]),
            underlying_price=float(HSI_test2['UndrlyingPr'][i]),
            pricing_model_type='BlackScholes',
            volatility_type='Implied',
            underlying_time_stamp='Default',
            report_ccy='HKD'
        ))
    for i in range(len(HSI_test2.index))]


for i, j in enumerate(Chunks(exp_hist_daily_universe_l, 100)):
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(exp_hist_daily_universe_l, 100)])} started")
    # Example request with Body Parameter - Symbology Lookup
    troubleshoot_resp_exp = rdf.Definitions(universe=j, fields=requestFields)
    troubleshoot_resp_exp_getdata = troubleshoot_resp_exp.get_data()
    if i == 0:
        troubleshoot_resp_exp_df = troubleshoot_resp_exp_getdata.data.df
    else:
        troubleshoot_resp_exp_df = troubleshoot_resp_exp_df.append(
            troubleshoot_resp_exp.data.df, ignore_index=True)
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(exp_hist_daily_universe_l, 100)])} ended")


troubleshoot_resp_exp_df

	DocumentTitle	RIC	StrikePrice	ExchangeCode	ExpiryDate	UnderlyingQuoteRIC	InsertDateTime	RetireDate
0	Eurex EURO STOXX 50 Index Option 4200 Call Apr...	STXE42000D3.EX	4200	EUX	2023-04-21	[.STOXX50E]	2023-03-09 04:00:21	2023-04-25
1	Eurex EURO STOXX 50 Index Option 4100 Call Apr...	STXE41000D3.EX	4100	EUX	2023-04-21	[.STOXX50E]	2023-03-09 03:59:34	2023-04-25
2	Eurex EURO STOXX 50 Index Option 4000 Call Apr...	STXE40000D3.EX	4000	EUX	2023-04-21	[.STOXX50E]	2023-03-09 03:55:59	2023-04-25
3	Eurex EURO STOXX 50 Index Option 4300 Call Apr...	STXE43000D3.EX	4300	EUX	2023-04-21	[.STOXX50E]	2023-03-09 03:59:58	2023-04-25
4	Eurex EURO STOXX 50 Index Option 4000 Call Jun...	STXE40000F3.EX	4000	EUX	2023-06-16	[.STOXX50E]	2023-03-09 03:59:37	2023-06-20
...	...	...	...	...	...	...	...	...
95	Eurex EURO STOXX 50 Index Option 4375 Call Jun...	STXE43750F3.EX	4375	EUX	2023-06-16	[.STOXX50E]	2023-03-09 04:05:43	2023-06-20
96	Eurex EURO STOXX 50 Index Option 3400 Call Apr...	STXE34000D3.EX	3400	EUX	2023-04-21	[.STOXX50E]	2023-03-09 04:47:24	2023-04-25
97	Eurex EURO STOXX 50 Index Option 3000 Call Apr...	STXE30000D3.EX	3000	EUX	2023-04-21	[.STOXX50E]	2023-03-09 04:01:09	2023-04-25
98	Eurex EURO STOXX 50 Index Option 2800 Call Apr...	STXE28000D3.EX	2800	EUX	2023-04-21	[.STOXX50E]	2023-03-09 04:25:58	2023-04-25
99	Eurex EURO STOXX 50 Index Option 4850 Call Apr...	STXE48500D3.EX	4850	EUX	2023-04-21	[.STOXX50E]	2023-03-09 04:12:59	2023-04-25

	DocumentTitle	RIC	StrikePrice	ExchangeCode	ExpiryDate	UnderlyingQuoteRIC	InsertDateTime	RetireDate
0	Eurex EURO STOXX 50 Index Option 4200 Call Apr...	STXE42000D3.EX	4200	EUX	2023-04-21	[.STOXX50E]	2023-03-09 04:00:21	2023-04-25
1	Eurex EURO STOXX 50 Index Option 4100 Call Apr...	STXE41000D3.EX	4100	EUX	2023-04-21	[.STOXX50E]	2023-03-09 03:59:34	2023-04-25
2	Eurex EURO STOXX 50 Index Option 4000 Call Apr...	STXE40000D3.EX	4000	EUX	2023-04-21	[.STOXX50E]	2023-03-09 03:55:59	2023-04-25
3	Eurex EURO STOXX 50 Index Option 4300 Call Apr...	STXE43000D3.EX	4300	EUX	2023-04-21	[.STOXX50E]	2023-03-09 03:59:58	2023-04-25
4	Eurex EURO STOXX 50 Index Option 4125 Call Apr...	STXE41250D3.EX	4125	EUX	2023-04-21	[.STOXX50E]	2023-03-09 03:50:14	2023-04-25
...	...	...	...	...	...	...	...	...
150	Eurex EURO STOXX 50 Index Option 1900 Call Apr...	STXE19000D3.EX	1900	EUX	2023-04-21	[.STOXX50E]	2023-03-09 04:22:12	2023-04-25
151	Eurex EURO STOXX 50 Index Option 7000 Call Apr...	STXE70000D3.EX	7000	EUX	2023-04-21	[.STOXX50E]	2023-03-09 05:50:32	2023-04-25
152	Eurex EURO STOXX 50 Index Option 8000 Call Apr...	STXE80000D3.EX	8000	EUX	2023-04-21	[.STOXX50E]	2023-03-09 06:17:00	2023-04-25
153	Eurex EURO STOXX 50 Index Option 9000 Call Apr...	STXE90000D3.EX	9000	EUX	2023-04-21	[.STOXX50E]	2023-03-09 06:11:01	2023-04-25
154	Eurex EURO STOXX 50 Index Option 10000 Call Ap...	STXE100000D3.EX	10000	EUX	2023-04-21	[.STOXX50E]	2023-03-09 06:03:00	2023-04-25

STXE42250D3.EX	IMP_VOLT
Timestamp
2022-12-28 00:53:31.808	15.6212
2022-12-29 00:53:36.379	15.3931
2022-12-30 00:53:41.120	15.2385
2022-12-31 00:53:31.819	15.3381
2023-01-03 00:53:39.716	15.8512

STXE42250D3.EX	TRDPRC_1
Timestamp
2023-03-23 10:10:00	65.7
2023-03-23 10:20:00	63.6
2023-03-23 10:30:00	63.6
2023-03-23 11:10:00	67.6
2023-03-23 13:00:00	70.0

STXE42250D3.EX	TRDPRC_1
Timestamp
2023-03-23 10:10:00	65.7
2023-03-23 10:20:00	63.6
2023-03-23 10:30:00	63.6
2023-03-23 10:40:00	63.6
2023-03-23 10:50:00	63.6
...	...
2023-03-29 10:50:00	65.9
2023-03-29 11:00:00	65.9
2023-03-29 11:10:00	65.9
2023-03-29 11:20:00	65.9
2023-03-29 11:30:00	68.4

.STOXX50E	TRDPRC_1
Timestamp
2023-03-22 14:50:00	4207.1
2023-03-22 15:00:00	4202.74
2023-03-22 15:10:00	4199.54
2023-03-22 15:20:00	4202.01
2023-03-22 15:30:00	4202.94
...	...
2023-03-29 12:00:00	4213.68
2023-03-29 12:10:00	4213.48
2023-03-29 12:20:00	4214.88
2023-03-29 12:30:00	4215.13
2023-03-29 12:40:00	4215.61

STXE42250D3.EX	underlying .STOXX50E TRDPRC_1	TRDPRC_1
Timestamp
2023-03-23 10:10:00	4176.5	65.7
2023-03-23 10:20:00	4170.06	63.6
2023-03-23 10:30:00	4169.96	63.6
2023-03-23 10:40:00	4170.47	63.6
2023-03-23 10:50:00	4171.68	63.6
...	...	...
2023-03-29 12:00:00	4213.68	68.4
2023-03-29 12:10:00	4213.48	68.4
2023-03-29 12:20:00	4214.88	68.4
2023-03-29 12:30:00	4215.13	68.4
2023-03-29 12:40:00	4215.61	68.4

EURIBOR3MD=	Fixing Value
Date
2023-03-29	3.015
2023-03-28	2.99
2023-03-27	3.012
2023-03-24	3.025
2023-03-23	2.99
2023-03-22	3.002
2023-03-21	2.908

STXE42250D3.EX	underlying .STOXX50E TRDPRC_1	TRDPRC_1	EurRfRate
2023-03-23 12:10:00+02:00	4176.5	65.7	2.99
2023-03-23 12:20:00+02:00	4170.06	63.6	2.99
2023-03-23 12:30:00+02:00	4169.96	63.6	2.99
2023-03-23 12:40:00+02:00	4170.47	63.6	2.99
2023-03-23 12:50:00+02:00	4171.68	63.6	2.99
...	...	...	...
2023-03-29 14:00:00+02:00	4213.68	68.4	2.99
2023-03-29 14:10:00+02:00	4213.48	68.4	2.99
2023-03-29 14:20:00+02:00	4214.88	68.4	2.99
2023-03-29 14:30:00+02:00	4215.13	68.4	2.99
2023-03-29 14:40:00+02:00	4215.61	68.4	2.99

	MarketValueInDealCcy	RiskFreeRatePercent	UnderlyingPrice	PricingModelType	DividendType	UnderlyingTimeStamp	ReportCcy	VolatilityType	Volatility	DeltaPercent	GammaPercent	RhoPercent	ThetaPercent	VegaPercent
0	65.7	2.99	4176.50	BlackScholes	ImpliedYield	Default	EUR	Calculated	20.650307	0.425992	0.001809	1.079711	-1.884532	4.106245
1	63.6	2.99	4170.06	BlackScholes	ImpliedYield	Default	EUR	Calculated	20.798941	0.415109	0.001789	1.050709	-1.883593	4.077227
2	63.6	2.99	4169.96	BlackScholes	ImpliedYield	Default	EUR	Calculated	20.809121	0.414981	0.001788	1.050346	-1.884306	4.076843
3	63.6	2.99	4170.47	BlackScholes	ImpliedYield	Default	EUR	Calculated	20.757180	0.415636	0.001793	1.052202	-1.880663	4.078800
4	63.6	2.99	4171.68	BlackScholes	ImpliedYield	Default	EUR	Calculated	20.633718	0.417204	0.001805	1.056641	-1.871975	4.083428
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
229	68.4	2.99	4213.68	BlackScholes	ImpliedYield	Default	EUR	Calculated	17.233868	0.488814	0.002185	1.254797	-1.626663	4.212997
230	68.4	2.99	4213.48	BlackScholes	ImpliedYield	Default	EUR	Calculated	17.257066	0.488415	0.002182	1.253675	-1.628624	4.212683
231	68.4	2.99	4214.88	BlackScholes	ImpliedYield	Default	EUR	Calculated	17.094316	0.491231	0.002203	1.261585	-1.614832	4.214797
232	68.4	2.99	4215.13	BlackScholes	ImpliedYield	Default	EUR	Calculated	17.065163	0.491740	0.002206	1.263013	-1.612353	4.215154
233	68.4	2.99	4215.61	BlackScholes	ImpliedYield	Default	EUR	Calculated	17.009111	0.492721	0.002213	1.265768	-1.607577	4.215820

STXE42000Q2.EX^E22	BID	ASK	TRDPRC_1	SETTLE
Date
2022-02-21	349.2	360.3	<NA>	353.8
2022-02-22	344.8	357.2	<NA>	349.3
2022-02-23	351.2	365.7	<NA>	358.9
2022-02-24	466.1	486.0	508.2	476.6
2022-02-25	353.0	366.0	<NA>	357.0
...	...	...	...	...
2022-05-16	511.5	543.3	NaN	527.1
2022-05-17	458.2	471.0	NaN	465.0
2022-05-18	504.7	534.2	NaN	518.8
2022-05-19	551.8	585.1	NaN	569.7
2022-05-20	477.6	540.8	NaN	500.2

HSI20200N3.HF^B23	SETTLE	TRDPRC_1	BID	ASK
Date
2022-11-08	3569	<NA>	<NA>	<NA>
2022-11-09	3713	<NA>	<NA>	<NA>
2022-11-10	4025	<NA>	<NA>	<NA>
2022-11-11	2939	<NA>	<NA>	<NA>
2022-11-14	2665	<NA>	<NA>	<NA>

SPXm212240000.U^A22	TRDPRC_1	BID	ASK
Date
2020-12-21	<NA>	484.50	494.00
2020-12-22	<NA>	491.10	495.10
2020-12-23	473.3	485.10	488.80
2020-12-24	<NA>	471.70	477.40
2020-12-28	<NA>	449.60	453.20
...	...	...	...
2022-01-13	0.57	0.55	0.65
2022-01-14	0.4	0.35	0.50
2022-01-18	0.4	0.35	0.45
2022-01-19	0.25	0.20	0.30
2022-01-20	0.15	0.05	0.15

HK3MT=RR	Mid Price
Date
2023-02-01	0.5925
2023-01-31	0.5745
2023-01-30	0.523
2023-01-27	0.532
2023-01-26	0.577
...	...
2022-11-14	0.71
2022-11-11	0.713
2022-11-10	0.731
2022-11-09	0.7365
2022-11-08	0.7445

	MarketValueInDealCcy	RiskFreeRatePercent	UnderlyingPrice	PricingModelType	DividendType	UnderlyingTimeStamp	ReportCcy	VolatilityType	Volatility	DeltaPercent	GammaPercent	RhoPercent	ThetaPercent	VegaPercent
0	None	None	None	None	None	None	None	None	None	None	None	None	None	None
1	None	None	None	None	None	None	None	None	None	None	None	None	None	None
2	None	None	None	None	None	None	None	None	None	None	None	None	None	None
3	None	None	None	None	None	None	None	None	None	None	None	None	None	None
4	None	None	None	None	None	None	None	None	None	None	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
56	None	None	None	None	None	None	None	None	None	None	None	None	None	None
57	None	None	None	None	None	None	None	None	None	None	None	None	None	None
58	None	None	None	None	None	None	None	None	None	None	None	None	None	None
59	None	None	None	None	None	None	None	None	None	None	None	None	None	None
60	None	None	None	None	None	None	None	None	None	None	None	None	None	None

	TRDPRC_1_OptPr	TRDPRC_1_UnderlyingPr	EurRfRate
Timestamp
2023-03-23 10:10:00	65.7	4176.5	2.990
2023-03-23 10:20:00	63.6	4170.06	2.990
2023-03-23 10:30:00	63.6	4169.96	2.990
2023-03-23 11:10:00	67.6	4184.29	2.990
2023-03-23 13:00:00	70.0	4186.71	2.990
2023-03-23 13:40:00	68.6	4191.93	2.990
2023-03-23 13:50:00	69.6	4197.17	2.990
2023-03-23 14:40:00	73.2	4206.26	2.990
2023-03-23 15:20:00	78.0	4207.85	2.990
2023-03-23 16:10:00	76.0	4205.64	2.990
2023-03-23 16:20:00	75.2	4205.84	2.990
2023-03-24 08:00:00	67.0	4155.74	3.025
2023-03-24 09:00:00	58.5	4153.61	3.025
2023-03-24 09:20:00	56.7	4142.65	3.025
2023-03-24 10:20:00	55.2	4119.19	3.025
2023-03-24 10:30:00	51.3	4114.7	3.025
2023-03-24 11:20:00	49.6	4110.14	3.025
2023-03-24 12:10:00	48.0	4104.0	3.025
2023-03-24 12:20:00	48.0	4111.43	3.025
2023-03-24 12:40:00	46.6	4110.48	3.025
2023-03-24 13:40:00	49.5	4121.7	3.025
2023-03-24 13:50:00	50.6	4134.15	3.025
2023-03-24 14:00:00	54.0	4126.4	3.025
2023-03-24 14:30:00	48.7	4124.55	3.025
2023-03-24 15:20:00	46.8	4118.08	3.025
2023-03-24 15:40:00	49.5	4129.03	3.025
2023-03-24 15:50:00	49.8	4136.98	3.025
2023-03-24 16:00:00	51.2	4132.09	3.025
2023-03-24 16:20:00	47.2	4124.25	3.025
2023-03-27 07:20:00	59.0	4166.37	3.012
2023-03-27 07:50:00	55.7	4147.46	3.012
2023-03-27 08:00:00	53.4	4154.24	3.012
2023-03-27 09:50:00	58.2	4166.21	3.012
2023-03-27 10:00:00	60.5	4172.21	3.012
2023-03-27 10:20:00	61.1	4173.36	3.012
2023-03-28 07:40:00	63.6	4189.97	2.990
2023-03-28 08:20:00	60.0	4181.98	2.990
2023-03-28 08:40:00	57.4	4176.89	2.990
2023-03-28 08:50:00	58.0	4176.46	2.990

	TRDPRC_1_OptPr	TRDPRC_1_UnderlyingPr	EurRfRate
2023-03-23 12:10:00+02:00	65.7	4176.5	2.990
2023-03-23 12:20:00+02:00	63.6	4170.06	2.990
2023-03-23 12:30:00+02:00	63.6	4169.96	2.990
2023-03-23 13:10:00+02:00	67.6	4184.29	2.990
2023-03-23 15:00:00+02:00	70.0	4186.71	2.990
2023-03-23 15:40:00+02:00	68.6	4191.93	2.990
2023-03-23 15:50:00+02:00	69.6	4197.17	2.990
2023-03-23 16:40:00+02:00	73.2	4206.26	2.990
2023-03-23 17:20:00+02:00	78.0	4207.85	2.990
2023-03-23 18:10:00+02:00	76.0	4205.64	2.990
2023-03-23 18:20:00+02:00	75.2	4205.84	2.990
2023-03-24 10:00:00+02:00	67.0	4155.74	3.025
2023-03-24 11:00:00+02:00	58.5	4153.61	3.025
2023-03-24 11:20:00+02:00	56.7	4142.65	3.025
2023-03-24 12:20:00+02:00	55.2	4119.19	3.025
2023-03-24 12:30:00+02:00	51.3	4114.7	3.025
2023-03-24 13:20:00+02:00	49.6	4110.14	3.025
2023-03-24 14:10:00+02:00	48.0	4104.0	3.025
2023-03-24 14:20:00+02:00	48.0	4111.43	3.025
2023-03-24 14:40:00+02:00	46.6	4110.48	3.025
2023-03-24 15:40:00+02:00	49.5	4121.7	3.025
2023-03-24 15:50:00+02:00	50.6	4134.15	3.025
2023-03-24 16:00:00+02:00	54.0	4126.4	3.025
2023-03-24 16:30:00+02:00	48.7	4124.55	3.025
2023-03-24 17:20:00+02:00	46.8	4118.08	3.025
2023-03-24 17:40:00+02:00	49.5	4129.03	3.025
2023-03-24 17:50:00+02:00	49.8	4136.98	3.025
2023-03-24 18:00:00+02:00	51.2	4132.09	3.025
2023-03-24 18:20:00+02:00	47.2	4124.25	3.025
2023-03-27 09:20:00+02:00	59.0	4166.37	3.012
2023-03-27 09:50:00+02:00	55.7	4147.46	3.012
2023-03-27 10:00:00+02:00	53.4	4154.24	3.012
2023-03-27 11:50:00+02:00	58.2	4166.21	3.012
2023-03-27 12:00:00+02:00	60.5	4172.21	3.012
2023-03-27 12:20:00+02:00	61.1	4173.36	3.012
2023-03-28 09:40:00+02:00	63.6	4189.97	2.990
2023-03-28 10:20:00+02:00	60.0	4181.98	2.990
2023-03-28 10:40:00+02:00	57.4	4176.89	2.990
2023-03-28 10:50:00+02:00	58.0	4176.46	2.990
2023-03-28 13:10:00+02:00	55.5	4170.95	2.990

	MarketValueInDealCcy	RiskFreeRatePercent	UnderlyingPrice	Volatility	DeltaPercent	GammaPercent	RhoPercent	ThetaPercent	VegaPercent
2023-03-14 09:10:00+01:00	110.0	2.753	4100.3	21.236716	-0.488709	0.001453	-2.084896	-1.539653	5.118502
2023-03-14 09:40:00+01:00	106.5	2.753	4114.63	21.891511	-0.468043	0.001401	-2.004483	-1.586342	5.122988
2023-03-14 09:50:00+01:00	106.3	2.753	4117.22	22.088216	-0.464473	0.001387	-1.990986	-1.599943	5.122486
2023-03-14 10:00:00+01:00	109.4	2.753	4106.97	21.749656	-0.478848	0.001416	-2.047576	-1.576564	5.122176
2023-03-14 10:40:00+01:00	110.5	2.753	4105.54	21.830447	-0.480837	0.001411	-2.056039	-1.582192	5.121585
...	...	...	...	...	...	...	...	...	...
2023-03-16 12:30:00+01:00	146.0	2.646	4068.51	25.086035	-0.525523	0.001238	-2.252807	-1.802243	5.068409
2023-03-16 12:40:00+01:00	139.0	2.646	4081.85	25.066443	-0.509055	0.001237	-2.186519	-1.809341	5.095103
2023-03-16 13:00:00+01:00	145.0	2.646	4064.63	24.483388	-0.53187	0.001268	-2.275254	-1.756661	5.05738
2023-03-16 13:20:00+01:00	146.3	2.646	4075.46	25.857575	-0.515421	0.0012	-2.216098	-1.861411	5.084274
2023-03-16 14:30:00+01:00	156.0	2.646	4052.57	25.377735	-0.54435	0.001223	-2.329659	-1.808759	5.026452

	MarketValueInDealCcy	RiskFreeRatePercent	UnderlyingPrice	Volatility	DeltaPercent	GammaPercent	RhoPercent	ThetaPercent	VegaPercent
2023-03-13 16:50:00+01:00	104.3	2.957	4115.42	24.080919	-0.432544	0.00126	-1.858586	-1.69381	5.069878
2023-03-14 10:50:00+01:00	104.2	2.753	4101.77	22.801912	-0.449826	0.001344	-1.922581	-1.619456	5.085256
2023-03-14 14:30:00+01:00	71.0	2.753	4177.94	22.234661	-0.348144	0.001265	-1.504628	-1.502993	4.841338
2023-03-14 16:10:00+01:00	66.1	2.753	4183.59	21.6196	-0.337516	0.001284	-1.457882	-1.446727	4.791758
2023-03-14 16:20:00+01:00	65.6	2.753	4186.8	21.740407	-0.334132	0.001271	-1.444482	-1.450137	4.776695
2023-03-15 10:10:00+01:00	83.6	2.815	4138.78	21.851357	-0.397499	0.001355	-1.705079	-1.524007	5.002307
2023-03-15 10:30:00+01:00	95.3	2.815	4114.14	22.145603	-0.431844	0.00137	-1.846323	-1.564368	5.066786
2023-03-15 10:50:00+01:00	106.7	2.815	4099.31	23.097975	-0.453025	0.001329	-1.936888	-1.637633	5.087092
2023-03-15 11:10:00+01:00	117.1	2.815	4083.15	23.668493	-0.47447	0.001308	-2.02629	-1.679064	5.091099
2023-03-15 11:20:00+01:00	124.9	2.815	4054.58	22.425543	-0.514369	0.001392	-2.180171	-1.582211	5.06069
2023-03-15 11:30:00+01:00	135.8	2.815	4058.52	24.973756	-0.504807	0.001249	-2.154644	-1.763101	5.069006
2023-03-15 11:40:00+01:00	133.0	2.815	4031.94	21.644943	-0.548569	0.00144	-2.312677	-1.508364	4.996483
2023-03-15 11:50:00+01:00	149.8	2.815	4032.16	25.025447	-0.537643	0.001249	-2.285916	-1.746851	5.012305
2023-03-15 12:00:00+01:00	134.9	2.815	4048.0	23.730194	-0.520573	0.001316	-2.211464	-1.669365	5.048714
2023-03-15 12:10:00+01:00	138.2	2.815	4052.73	24.866305	-0.512238	0.001256	-2.18383	-1.752234	5.059391
2023-03-15 12:20:00+01:00	138.8	2.815	4046.17	24.313957	-0.521676	0.001285	-2.218772	-1.708988	5.045634
2023-03-15 12:40:00+01:00	135.6	2.815	4047.99	23.86781	-0.520288	0.001309	-2.21101	-1.679015	5.048902
2023-03-15 13:00:00+01:00	139.5	2.815	4042.16	24.035525	-0.527506	0.0013	-2.240644	-1.686291	5.035775
2023-03-15 13:20:00+01:00	132.7	2.815	4047.12	23.203357	-0.522922	0.001346	-2.218221	-1.631741	5.04587
2023-03-15 13:30:00+01:00	138.8	2.815	4036.95	23.345265	-0.536205	0.001338	-2.271881	-1.6332	5.020024
2023-03-15 13:40:00+01:00	146.0	2.815	4033.34	24.393987	-0.537937	0.001281	-2.28396	-1.703567	5.013404
2023-03-15 14:00:00+01:00	145.5	2.815	4037.11	24.696302	-0.532311	0.001266	-2.263066	-1.728365	5.024668
2023-03-15 14:10:00+01:00	144.2	2.815	4037.97	24.528659	-0.531655	0.001274	-2.259622	-1.717352	5.026439
2023-03-15 14:30:00+01:00	141.2	2.815	4050.48	25.231128	-0.514373	0.001238	-2.194182	-1.776359	5.055571
2023-03-15 14:40:00+01:00	129.7	2.815	4058.07	23.725309	-0.507331	0.001315	-2.158506	-1.675358	5.067832
2023-03-15 15:00:00+01:00	142.9	2.815	4061.81	26.699094	-0.49847	0.001168	-2.137899	-1.885611	5.073753
2023-03-15 15:10:00+01:00	131.1	2.815	4059.65	24.159177	-0.504595	0.001291	-2.149723	-1.706595	5.070459
2023-03-15 15:20:00+01:00	130.3	2.815	4058.34	23.870707	-0.506743	0.001307	-2.15688	-1.685695	5.06833
2023-03-15 15:30:00+01:00	135.3	2.815	4048.92	23.904098	-0.518994	0.001307	-2.206025	-1.682223	5.050936
2023-03-15 15:40:00+01:00	141.4	2.815	4040.46	24.234535	-0.529214	0.00129	-2.248438	-1.698849	5.032029
2023-03-15 15:50:00+01:00	144.4	2.815	4056.21	26.440594	-0.505403	0.001181	-2.164359	-1.86462	5.065995
2023-03-15 16:10:00+01:00	138.3	2.815	4070.16	26.604919	-0.488813	0.001169	-2.098699	-1.882361	5.082676
2023-03-15 16:20:00+01:00	134.3	2.815	4062.26	25.048471	-0.500038	0.001245	-2.13592	-1.770179	5.074277
2023-03-15 16:40:00+01:00	139.7	2.815	4067.62	26.635367	-0.491754	0.001169	-2.110653	-1.883597	5.08028
2023-03-15 17:10:00+01:00	149.0	2.815	4041.09	25.810029	-0.524676	0.001212	-2.238176	-1.810007	5.036936
2023-03-16 09:20:00+01:00	120.2	2.646	4084.07	24.297546	-0.473989	0.001274	-2.027841	-1.732824	5.091869
2023-03-16 09:30:00+01:00	121.4	2.646	4087.56	24.85653	-0.469508	0.001243	-2.012591	-1.772236	5.092349
2023-03-16 10:20:00+01:00	149.1	2.646	4056.55	27.331879	-0.504446	0.001142	-2.165337	-1.937077	5.066616
2023-03-16 13:10:00+01:00	132.8	2.646	4070.91	25.527232	-0.489669	0.001218	-2.097074	-1.816736	5.083867
2023-03-16 13:30:00+01:00	133.0	2.646	4068.9	25.372439	-0.492274	0.001227	-2.106754	-1.805244	5.081987

STXE42000D3.EX	OptionPrice	RfRatePrct	UndrlyingPr
Date
2022-11-09	37.8	0.7365	16358.52
2022-11-10	59.5	0.731	16081.04
2022-11-11	65.2	0.713	17325.66
2022-11-14	66.1	0.71	17619.71
2022-11-15	71.5	0.7965	18343.12
...	...	...	...
2023-01-26	112.8	0.577	22566.78
2023-01-27	109.9	0.532	22688.9
2023-01-30	104.4	0.523	22069.73
2023-01-31	104.7	0.5745	21842.33
2023-02-01	106.4	0.5925	22072.18

Implied Volatility and Greeks of Index Options¶

EUREX Call Options¶

Finding Live Options (using Search API)¶

Function for Expiration days¶

Function to find the next expiring Option outside the next x day window¶

Refinitiv-provided Daily Implied Volatility¶

Option Price¶

Underlying Asset Price¶

Opening Times Of Any One Exchange¶

Strike Price¶

Risk-Free Interest Rate¶

Annualized Continuous Dividend Rate¶

Calculating IV¶

All Trades¶

Delivery Layer¶

Content Layer¶

At Option Trade Only¶

Delivery Layer¶

Content Layer¶

Graphs¶

Overlay¶

Sack of 3 Graphs¶

Simple Graph¶

EUREX, or SPX Call or Put Options¶

Finding Expired Options¶

STOXX50E Usecase¶

General Usecase¶

General Use Case Test with HSI¶

General Use Case Test with SPX¶

Implied Volatility and Greeks of Expired Options (Historical Daily series)¶

Content Layer¶

Delivery Layer¶

Creating a class with PEP 3107 (a.k.a.: Type Hints)¶

index_imp_vola_and_greeks_IPA_calc Overlay Test¶

index_imp_vola_and_greeks_IPA_calc Simple Graph Test¶

dash_tvlwc¶

Conclusion¶

References¶

Q&A¶

Appendix¶

No Intra Day Price Data Is Available For Old Expired Options¶

Equity Option Price Adjustments to Corporate Actions¶

Troubleshooting IPA¶

Delivery Layer¶

Live Option¶

Expired Option¶

Content Layer¶

`index_imp_vola_and_greeks_IPA_calc` Overlay Test¶

`index_imp_vola_and_greeks_IPA_calc` Simple Graph Test¶